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Data intelligence is the process of extracting actionable insights from unstructured, raw data such as text, scanned documents, and PDFs. With businesses handling large amounts of data everyday, the ability to process and analyze documents efficiently is important. Manual data processing is slow, prone to error, and not scalable. Hence, automation in the knowledge extraction from the documents is essential.  Industries like healthcare, legal, and finance, heavily depend on document intelligence for tasks like summarizing contracts, extracting customer data, and analyzing invoices. Automating knowledge extraction saves time, reduces human error, and provides businesses with accurate, structured data for decision making. This blog explores how document intelligence works, its challenges, and how tools like Encord improve automation for precise and scalable document annotation. What is Document Intelligence? Document intelligence focuses on using artificial intelligence and natural language processing to convert unstructured text into structured, usable formats. It enables businesses to extract valuable information from complex datasets and streamline processes that traditionally require significant manual effort. These structured data can either be used by the team for data analysis or used to build machine learning algorithms.  Source Key Applications of Document Intelligence Text Classification: Categorizing documents into predefined groups, such as labeling emails as "urgent" or "non-urgent." Named Entity Recognition (NER): Identifying specific entities like names, dates, monetary amounts, and locations in documents. Sentiment Analysis: Analyzing text for emotional tone, such as determining customer feedback sentiment. Summarization: Condensing long documents into concise summaries. Data Extraction: Extracting key values or fields from documents, such as invoice numbers or legal clauses. Real World Use Cases Automate Data Entry for Analytics and Operations Document intelligence simplifies data extraction, helping businesses to automate tedious data entry processes. This is particularly useful in industries such as shipping, procurement, financial services, mortgage processing, and the mail room, where large volumes of documents need to be processed quickly and accurately. By automating the extraction of critical data points like invoice numbers, order details, or customer addresses, businesses can reduce manual errors and improve overall efficiency. This extracted data can then be fed into business systems for analytics for data-backed decision-making. Data Analysis By integrating knowledge extraction models with analytics platforms like BigQuery, you can get deeper insights from their documents. The models extract metadata from the documents and automatically load it into structured tables. This combines the structured and unstructured data, enabling advanced analytics that was not possible previously. For example, by joining document data with sales data, companies can uncover patterns that inform marketing and sales strategies. Document Classification for Workflow Management The knowledge extraction models are trained to assign categories or labels to the documents. This categorization ensures that the documents are routed to the appropriate team or department for further action, making them easier to search, filter, and analyze. This document management reduces the time spent on manual sorting and speeds up the decision making process. Read the blog on Data classification for more information. Improving Data Processing with AI SaaS customers and independent software vendors (ISVs) are increasingly using automated knowledge extraction models to improve document processing solutions. These models not only extract and categorize information but also generate responses or perform advanced analysis based on content of the document. This offers significant value for applications like customer support, compliance monitoring and document review. Digitizing Text for ML Model Training With optical character recognition (OCR) in the workflows, businesses can convert scanned or handwritten documents, reports, and presentations into machine readable formats. This makes archival content usable for training models tailored to specific business needs, such as predictive maintenance or customer behaviour forecasting. By transforming previously inaccessible data into structured information, these models enable faster and more efficient model development. Source Building Generative AI with Document Data Automated knowledge extraction models are critical in feeding valuable document data into generative AI systems. By combining OCR and natural language processing with advanced AI frameworks like Gemini or GPT APIs organizations can access capabilities such as document Q&A experiences, automated document comparison, or document generation.  Challenges in Manual Knowledge Extraction Unstructured Data Complexity Unstructured documents are inherently messy. Documents like contracts, invoices, or emails have varying formats, inconsistent layouts, and ambiguous language. Scanned documents and handwritten notes further complicate the process by requiring additional preprocessing like OCR. Human Error and Inconsistency Manual annotation heavily relies on individual effort which introduces variability and errors, especially when scaling across thousands of documents. Two annotators may interpret and classify the same text differently, leading to inconsistent data. Time Consuming  Extracting insights manually from large datasets need significant time and resources. For example, reviewing 1000 contracts manually could take weeks, whereas automated tools can complete the same task in hours or even minutes. Scalability Issues As the amount of data grows, manual methods cannot keep up. Scaling knowledge extraction to process millions of documents required automation to maintain speed and quality. High Costs Manual processes often require hiring large teams of annotators and quality assurance, driving up the operational costs. This can be burdensome for industries with tight margins, like retail and small-scale legal firms. Benefits of Intelligent Document Processing Better Efficiency Automating document workflows eliminates the need for time consuming manual data entry and processing. Tasks such as extracting key fields or categorizing large batches of documents can be completed in less time, reducing turnaround times and allowing you to focus on higher value tasks. Improved Accuracy AI-powered document automation reduces errors commonly associated with manual processes. Models trained for OCR, NER, and classification ensure precise extraction of data. For instance, in financial processing, automation minimizes discrepancies in invoice matching, improving operational accuracy and compliance. Scalability Document intelligence automation systems can handle vast amounts of data, making them ideal for industries such as banking, insurance, and healthcare. Whether it's processing thousands of claims, customer applications, or research documents, these systems scale effortlessly, maintaining consistency and accuracy across all inputs. Better Compliance and Auditability In industries where compliance is critical, document automation ensures that all necessary data is captured, stored, and retrievable for audits. By maintaining an accurate digital trail of processed documents, businesses can easily demonstrate regulatory adherence and avoid potential penalties. Actionable Insights through Analytics Document intelligence automation doesn't stop at data extraction, it allows businesses to analyze the extracted information to get actionable insights. By integrating with tools like analytics platforms, you can uncover trends, patterns, and opportunities hidden within the documents for smarter strategic decisions. Automating Knowledge Extraction: Key Steps Here’s an overview of the main steps in the process: Document Ingestion and Preprocessing The first step in automating knowledge extraction from documents is data curation and ingesting documents into the system. This involves collecting various types of documents from different sources, such as emails, PDFs, scanned images, and digital files. Once done, preprocessing takes place. This includes converting scanned documents into machine readable text using OCT, removing noise from images from scanned documents, or standardizing document formats.  Text Parsing and Layout Analysis This step involves analyzing the structure of the document, such as identifying headings, paragraphs, tables, or bullet points. The models break down the document into logical components or key value pairs that are easier to interpret. For example, in a legal contract, the system would identify clauses, parties involved, and dates.  Layout analysis helps in understanding the spatial arrangement of elements in the document, allowing the extraction model to focus on relevant areas and avoid irrelevant content. Named Entity Recognition (NER) NER identifies specific entities within the document, such as dates, names, monetary values, locations, and other key terms. NER makes sure that only the relevant pieces of information are extracted and structured. This helps to transform raw documents into valuable data for subsequent document analysis or action. Data Extraction and Structuring At this stage, the automated models extract text from the documents. This involves getting key fields like invoice numbers, payment terms, and order quantities from invoices or extracting medical details such as patient names, diagnoses, and treatment dates from medical records. The extracted data is then structured into a usable format with the help of predefined templates, such as a database entry, a table, or a spreadsheet.  Data Classification and Annotation In order to ensure the documents are appropriately organized and processed, the automated knowledge extractors use classification models to classify and annotate documents. By classifying the documents, you streamline the workflows ensuring that the documents are directed to the correct team or department.  Post-Processing and Integration After the data has been extracted, it often goes through post processing to ensure that the extraction is accurate and ready for use. This step involves cleaning the data, validating it against predetermined rules, or formatting it for integration for real world use cases. For instance, the extracted data might be transferred into a CRM, accounting software, or a data warehouse for further analysis. Integration with other systems allows businesses to make use of the extracted data immediately, allowing decision making, reporting, and analytics. Continuous Learning and Improvement One of the most valuable aspects of automated knowledge extraction systems is their ability to learn and improve over time. ML models used for document processing are often designed to continuously refine their accuracy based on new data. As more documents are processed, the system can fine-tune its models to recognize patterns, improve data extraction, and handle new document types. This ongoing learning process ensures that the system becomes more efficient and accurate as it is used, providing increasing value over time. How Encord Enhances Knowledge Extraction Encord is a comprehensive platform designed to streamline the data annotation process for machine learning projects. It provides advanced tools for creating, managing, and scaling annotation workflows across various data types, including text, images, audio, and video.  Here's how Encord simplifies and accelerates knowledge extraction: Customizable Annotation Workflows Encord allows users to create tailored annotation workflows for specific tasks such as Named Entity Recognition (NER), text classification, and sentiment analysis. These workflows are fully adaptable allowing you to define custom labeling schemas, rules, and automation steps that suit your unique data and project needs.  Scalability and Collaboration The platform is built to handle large datasets, making it ideal for projects that require extensive document processing. It supports team-based workflows, allowing multiple annotators to collaborate efficiently. With features like task assignment, progress tracking, and real-time updates, teams can work together easily, even on complex projects. This scalability ensures that enterprises can annotate thousands of documents without sacrificing accuracy or speed. Integration with AI Models One of Encord’s key strengths is its compatibility with both pre-trained and fine-tuned AI models.  You can easily integrate your prebuilt models into the annotation pipeline to automate repetitive tasks, such as initial labeling or data preprocessing. This integration accelerates workflows and improves the quality of annotations by using model predictions for human validation and review.  Quality Assurance Mechanisms Encord offers built-in quality assurance features to ensure labeling consistency across projects. It provides metrics to assess the quality of the annotated data. It also provides features like automated validation checks, inter-annotator agreement metrics, and review workflows to help maintain high data quality. These features reduce errors and ensure that the extracted knowledge is reliable and ready for downstream AI applications. Conclusion Document intelligence automation is revolutionizing the way businesses process and utilize unstructured data. With algorithms like NLP, OCR, and AI, you can transform time consuming manual workflows into efficient, scalable, and highly accurate processes. From automating data entry to generating actionable insights, the benefits of document intelligence automation are undeniable across industries like healthcare, finance, and legal. However, implementing these solutions requires the right tools. Platforms like Encord help businesses to overcome challenges like data complexity, human error, and scalability, making knowledge extraction reliable. By combining customizable workflows, robust quality assurance, and AI integration, Encord not only accelerates document processing but also ensures you get maximum information from your curated data.  As the demand for intelligent automation grows, adopting document intelligence solutions will no longer be optional—it will be a necessity for staying competitive in today’s data-driven world. Start transforming your document workflows today and unlock the potential of automated knowledge extraction.

Open-source AI models are rapidly closing the gap with proprietary systems, and DeepSeek AI is at the forefront of this shift. DeepSeek is a Chinese AI company founded by Liang Wenfeng that focuses on building open source large language models (LLMs). With models like DeepSeek V3, Janus for image generation, and DeepSeek R1 for reasoning, DeepSeek has built a suite of AI tools that rival—or even outperform—closed models like OpenAI’s GPT-4 and Google’s Gemini or open source models like Meta’s Llama or Qwen. This blog explains DeepSeek’s key models, their features, what makes them stand out and how they compare to other top AI systems. DeepSeek V3 DeepSeek V3 is a Mixture of Experts (MoE) language model. Unlike dense models like GPT-4, where all the parameters are used for each and every token, MoE models selectively activate a subset of the model for each token. This version is also significant as it is a 671 billion parameter model but uses 37 billion parameters per token during inference. This means DeepSeek v3 doesn’t need the full model to be active at once, it only needs 37 billion parameters active per token. This makes the model more computationally efficient than a fully dense model of the same size. Model Architecture DeepSeek V3 is based on a Mixture of Experts (MoE) transformer architecture, which selectively activates different subsets of parameters for different inputs. Basic architecture of DeepSeek V3. Source. Key components of its architecture include: Mixture of Experts (MoE) Framework DeepSeek V3 follows an MoE-based architecture, where different "expert" subnetworks handle different parts of the computation. Instead of using all parameters for every token (as in dense models), DeepSeek V3 selects a subset of experts dynamically, reducing computational costs at a fraction of the cost of a fully dense model. This design allows the model to scale efficiently while keeping inference more resource-efficient. Multi-Head Latent Attention (MLA) The model incorporates Multi-Head Latent Attention (MLA), an approach used in DeepSeek V2. MLA optimizes attention mechanisms to make inference faster and more memory-efficient. DeepSeekMoE for Training Optimization DeepSeekMoE, introduced in earlier versions, is used to train the MoE layers efficiently. It helps distribute workload across experts, reducing imbalances that could affect model performance. Load Balancing Strategy MoE models often struggle with uneven expert utilization, which can slow down training. DeepSeek V3 introduces an auxiliary-loss-free load balancing strategy, which reduces the trade-offs between performance and even expert activation. Multi-Token Prediction (MTP) Training Instead of predicting one token at a time, DeepSeek V3 uses Multi-Token Prediction (MTP). This allows the model to predict multiple tokens in parallel, improving efficiency and potentially speeding up inference. Memory Optimization for Large-Scale Training DeepSeek V3 is designed to be trained without tensor parallelism, which typically requires extra memory and computing resources. This allows for higher training efficiency on GPUs at a low-cost, making it more accessible for large-scale deployments. These optimizations enable DeepSeek V3 to achieve strong performance with lower training and inference costs, making it a competitive open-source alternative to closed-source models like GPT-4o and Claude-3.5. Key Capabilities Computational Efficiency – The MoE structure reduces the number of active parameters per token, improving efficiency while maintaining strong performance. Extended Context Handling – Supports 128,000 tokens, allowing better processing of long documents and multi-turn conversations. Training Data and Fine-Tuning – Pretrained on 14.8 trillion tokens across multiple languages, with a focus on math and programming tasks. The model is then fine-tuned using Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF) for better reasoning and instruction following. Performance DeepSeek V3 achieves state of the art performance against open-source model on knowledge, reasoning, coding and math benchmarks. It scores 88.5 on MMLU, 75.9 on MMLU-Pro, and 59.1 on GPQA, surpassing other open models and closer to GPT-4o and Claude-3.5 performance. It excels in math, outperforming OpenAI’s o1-preview on MATH-500 and coding , ranking highest on LiveCodeBench. Source Its 128K token context length enables better long-form understanding. While closed models still lead in some areas, DeepSeek V3 offers a strong open-source alternative with competitive performance across multiple domains. For more information, read the DeepSeek-V3 Technical Report. You can try it now on DeepSeek Chat and find the model weights on Hugging Face or GitHub DeepSeek Multimodal Understanding and Generation Janus Janus is an autoregressive framework designed for multimodal tasks, combining both understanding and generation in a single generative AI model. It introduces a decoupled visual encoding approach, where separate pathways handle different aspects of visual processing while maintaining a unified transformer-based architecture. This design resolves conflicts between understanding and generation, making Janus more flexible than previous unified models.As a result, it matches or surpasses task-specific models in various multimodal benchmarks, demonstrating its effectiveness in vision-language tasks. Source For more information read the paper on arXiv, Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation Janus-Pro Janus-Pro builds on Janus with larger model scaling, improved training strategies, and expanded training data, leading to better multimodal understanding and more reliable text-to-image generation. These enhancements improve instruction-following capabilities for text-to-image tasks while increasing overall model stability. With these refinements, Janus-Pro pushes the performance of unified multimodal models further, offering a scalable and efficient solution for complex vision-language interactions. Key Capabilities Janus Unified Multimodal Model: Janus integrates both multimodal understanding and generation into a single model, addressing limitations of previous approaches. Decoupled Visual Encoding: By separating visual encoding into distinct pathways, Janus improves flexibility and performance for both understanding and generation tasks. Autoregressive Framework: Janus uses an autoregressive framework that leverages a unified transformer architecture for multimodal processing. Janus-Pro Enhanced Text-to-Image Instruction-Following: Janus-Pro significantly improves performance in generating images based on text instructions, achieving high scores on the GenEval leaderboard. Expanded Training Data and Larger Model Size: By scaling up the model size and increasing the dataset, Janus-Pro enhances stability and quality in text-to-image generation. Optimized Training Strategy: Janus-Pro incorporates a more refined training strategy for better performance on diverse multimodal tasks. Scalability: Janus-Pro supports multiple model sizes (1B and 7B parameters), showcasing its scalability in handling more complex tasks. Source Performance Janus-Pro significantly improves multimodal understanding and text-to-image generation over its predecessor, Janus. The Janus-Pro-7B model achieves a 79.2 score on MMBench, outperforming Janus (69.4), TokenFlow (68.9), and MetaMorph (75.2), demonstrating its superior multimodal reasoning capabilities. In text-to-image instruction-following, Janus-Pro-7B scores 0.80 on GenEval, surpassing Janus (0.61), DALL-E 3 (0.67), and Stable Diffusion 3 Medium (0.74). Source These improvements result from enhanced training strategies, expanded datasets, and increased model scale, making Janus-Pro a state-of-the-art unified multimodal model with strong generalization across tasks. For more information, visit the Janus project page on GitHub. You can also find the Janus-Pro-7B, Janus-Pro-1B, Janus-1.3B model weights on Hugging Face. DeepSeek R1 DeepSeek-R1 is an open-source reasoning model that matches OpenAI-o1 in math, reasoning, and code tasks. It presents a novel approach to reasoning tasks by using reinforcement learning(RL) for self evolution, while offering high performance solutions. Model Architecture It operates on the framework of the base model of DeepSeek V3. It uses RL for training without relying on supervised fine-tuning(SFT). IT starts with DeepSeek-R1-Zero, a model trained purely through RL, which naturally develops powerful reasoning behavior like self-verification, reflection, and chain-of-thought(CoT) solutions. Then the model is fine-tuned through a multi-stage training pipeline that incorporates cold-start data and SFt data from domains like writing and factual QA. This iterative process improves the model’s performance and helps resolve challenges such as readability and language mixing found in the initial RL phase. Key Capabilities Pure RL Training: Unlike most artificial intelligence models that rely on supervised fine-tuning, DeepSeek-R1 is primarily trained through RL. This means that the model self-evolves its reasoning capabilities. Self-Verification and Chain-of-Thought: The R1 model naturally develops advanced reasoning behaviors such as self-verification, reflection, and chain-of-thought solutions, improving its ability to solve complex tasks. Distilled Models: DeepSeek-R1 also includes distilled versions, such as DeepSeek-R1-Distill-Qwen-32B, offering competitive performance with reduced resource requirements. Performance DeepSeek-R1 matches or exceeds the performance of many SOTA models across a range of math, reasoning, and code tasks. The model achieves impressive results on reasoning benchmarks, setting new records for dense models, particularly with the distilled Qwen and Llama-based versions. For example, the DeepSeek-R1-Distill-Qwen-32B model surpasses OpenAI-o1-mini in various benchmarks. Source For more information, read the paper DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning. You can find the model weights on Hugging Face and visit the project page on Github. Key Takeaways DeepSeek models are fully open-source, fostering innovation and offering scalable, cost-effective solutions for diverse AI applications. DeepSeek V3: Utilizes a Mixture of Experts (MoE) architecture for computational efficiency, offering strong performance with reduced resource usage. Janus and Janus-Pro: Multimodal models that excel in both understanding and text-to-image generation with improved instruction-following capabilities and superior multimodal reasoning, making them powerful for AI assistants and chatbot applications. DeepSeek R1: A reasoning-focused model trained through reinforcement learning, achieving top performance in math, reasoning, and coding tasks. DeepSeek's models offer competitive performance to closed-source systems like GPT-4 and Claude-3.5, providing a high-performance alternative with efficient computing power usage. Register for our webinar "DeepSeek R1: How it works, What it means, and What comes next?" taking place on February 18 at 5:00 BST.

The success of artificial intelligence (AI) models depends heavily on the data. Poor data quality can degrade your model’s performance and cause customers to lose trust in your applications. According to a Gartner report, on average, low-quality data costs organizations around USD 12.9 million annually.  However, maintaining data quality becomes challenging as data volume and variety increase. The task requires organizations to build robust preprocessing and governance frameworks to ensure data is clean, consistent, and accurate. In particular, one of the challenges they face is detecting and resolving data anomalies.  This post will explore anomaly detection, its applications, techniques, and challenges. We will also see how Encord can help streamline anomaly detection workflows.  Why Anomaly Detection? Anomaly detection algorithms (AD) forecast unusual behaviors that do not align with the expected outcome. These points in overall data lie far away from the normal data. For instance, consider a patient X-ray where physicians diagnose the presence of a disease such as pneumonia or kidney stone. Anomaly  AD is key to ensuring the data used for decision-making is accurate and trustworthy. It helps: Improve Data Insights: In today’s digital age, data is abundant, and every decision is based on its insights. An accurate AD system ensures the data is clean, reliable, and anomaly-free. Save Costs: Flawed or skewed data can lead to poor decision-making, which may result in costly mistakes. By detecting and correcting anomalies, AD systems help ensure business decisions are based on accurate data, reducing the risk of financial loss. Types of Anomaly Detection Anomalies can be intentional or unintentional. Each type requires a unique fix based on the use case. Let’s explore these categories in more detail to understand their key differences.   Unintentional Anomalies Noise and errors in the data cause unintentional anomalies. These abnormalities can be random or systemic errors due to faulty sensors or human errors in data curation. They make it harder to draw accurate insights from the data.  Intentional Anomalies  Intentional anomalies are data points that deviate from normal behavior due to planned actions or specific events. Unlike random outliers, these anomalies provide valuable insights by highlighting unusual but predictable occurrences.  For instance, retailers often anticipate a sales spike during peak seasons like Black Friday or Christmas, where planned promotions intentionally create a surge in sales. While this may appear as an anomaly in general data, it is an expected event. Anomalies in Time Series Data Anomalies in time series data can be point-based, collective, or contextual. Point Anomalies: These are individual data points that deviate from the rest. Errors, random fluctuations, or unusual events may cause them. For example, a patient’s blood pressure suddenly increases well above its usual range, indicating a potential health issue. Collective Anomalies: A group of data points that, together, deviate from the norm, even though each point may seem normal on its own. For instance, a normally varied set of purchases on an online clothing store suddenly sees many customers buying the same jacket at once. Contextual Anomalies: Data points that appear normal but become anomalous when viewed in context. For example, a temperature of 10°C may be normal in some regions during winter but unusual in others. Anomaly Detection: Use Cases Across Industries Anomaly detection has a wide range of applications across the industry. Let’s go over each briefly.  Finance Sector   AD can help identify fraudulent transactions, security breaches, and other financial inconsistencies. For instance, based on users’ historical data, it can help banks detect credit card fraud, trading irregularities, and money laundering incidents. Manufacturing  AD helps in monitoring equipment and machine performance in the manufacturing domain. It can identify potential failures before they disrupt operations and cause costly downtime. For example, anomaly detection systems can continuously analyze machine sensor data to detect unusual patterns in predictive maintenance, such as unexpected temperature fluctuations or vibration trends.  Maintenance teams can be involved by forecasting potential issues and flagging these anomalies in a timely manner before a critical failure occurs. Healthcare Anomaly detection can help reveal unusual patterns in patient health data, such as the presence of a disease in medical reports or errors in data acquisition. For instance, if a patient’s heart rate suddenly deviates from the normal pattern, the AD system can alert the doctor for immediate examination and treatment. Cybersecurity In the cybersecurity domain, AD algorithms often help in intrusion detection to identify suspicious network traffic or potential malware threats. For instance, a popular application is flagging email as spam if it contains a suspicious email address or comes from an unknown sender. Anomaly Detection Techniques Several AD detection methods exist for recognizing data outliers, including statistical, machine learning, and deep learning approaches. Let’s examine each of these techniques. Statistical Methods  Statistical methods detect anomalies by identifying data points that deviate from expected trends using metrics like mean, z-scores, and standard deviations. These methods are generally easy to implement when the dataset size is small or follows a specific data distribution. Standard techniques include interquartile range (IQR) and percentiles.  Percentile Method: Marks a data point that falls outside a specific percentile range based on observed data patterns. By Author The figure above illustrates the percentile anomaly by showing the data points outside the 1st and 99th percentile range.  IQR method: labels data points outside the data's first and third quartile as anomalies.  By Author The figure illustrates the IQR method. It marks any data point outside the specified IQR range as an outlier, highlighting them in red circles.  Machine Learning Techniques ML AD techniques are categorized into traditional supervised and unsupervised approaches as well as more advanced DL techniques. They require training data to build a learning classifier that separates expected data points from outliers.  Supervised Learning Methods Supervised learning methods use labeled data that include normal and anomalous data points. The algorithm learns to draw a decision boundary between these data points. K-nearest neighbors (K-NN) and support vector machines (SVMs) are well-known examples. By Author K-NN identifies anomalies based on the distance between a data point and its neighbors. A point is considered anomalous if it is far from its nearest neighbors, indicating that it is different from the majority of the data.  SVM, in contrast, is a large-margin classifier that classifies data by finding a hyperplane that separates different classes. The hyperplane is the decision boundary that maximizes the distance between the nearest point of each class.  Unsupervised Learning Methods Unsupervised models use unlabeled data to detect anomalies. They include methods like the Isolation Forest, local outlier factor (lof), and k-means.algorithm. They detect unusual patterns in data without knowing the expected outcome.  However, unsupervised algorithms do not indicate the significance of a particular anomaly. For instance, an unsupervised model may highlight anomalies in an X-ray image dataset. Since the anomalous images have no labels attached, there’s no way to directly associate the anomaly with a severity level or risk factor. This limitation can lead to false positives and false negatives. Popular unsupervised AD algorithms are  Deep Learning (DL) Methods Unlike classical ML methods, which rely on simpler algorithms, DL techniques use more advanced and complex algorithms. While ML techniques may compute straightforward decision boundaries, DL involves neural networks for extracting intricate features within the data. An Autoencoder (AE) is a DL architecture often used for AD. It consists of an encoder, which maps the input data to a lower-dimensional latent space, and a decoder, which reconstructs the original data from this latent representation. It uses the reconstruction loss to assign an anomaly score. A higher score indicates the presence of significant anomalies in the data sample. Anomaly Detection Using Auto-Encoder As the figure indicates, a data point is marked as an anomaly if the reconstruction error exceeds the threshold.  Building an Anomaly Detection Pipeline Despite the above mentioned methods, detecting anomalies in extensive datasets can be challenging. A more efficient approach is to develop a robust detection pipeline that automatically flags anomalies as they occur. While the exact steps to build a pipeline can vary from case to case, the guidelines below provide a practical starting point. Identify Objectives and Define Expectations Start by defining what types of anomalies you want to detect. This step also involves setting clear expectations for metrics like accuracy and false positive rate. The approach will ensure your pipeline detects anomalies according to your project’s requirements. Data Collection and Preprocessing Data collection and preprocessing are the next steps. It  consists of the following: Data Acquisition: Acquire data that includes both normal and abnormal data. For instance, fraudulent activity detection may include transaction histories, known fraud cases, and user activities.  Data Cleaning: Normalizing data spanning over different scales, handling missing values, properly addressing outlier values in the data, and validating data accuracy.  Feature Engineering: Identifying features that capture the nuances in the data, creating new features, or addressing redundant features in a more compact feature space.  Select the Appropriate Model for Training Selecting an appropriate model for your application is critical. Use classical ML techniques where low latency is desired. However, you can use more advanced algorithms if the objective is high accuracy. Let’s discuss some of the state-of-the-art (SOTA) models that could be used for building robust AD systems.  Variational Autoencoders (VAE): VAEs are a more advanced form of AE that analyzes complex patterns in the data. VAE addresses the issue of an unregularized latent space. This means the VAE encoder also returns parameters of the latent distribution, such as the mean and standard deviation for each input. Unlike a regular autoencoder (AE), which learns direct mapping, VAE learns a probabilistic distribution over the latent space, enabling better generalization and sampling.  VAE for Anomaly Detection Encoder transforms high dimensional data (X) to latent space Z. Z with some mean and standard deviation projected back to the original data (X’). These architectures are suitable where the data is large and high accuracy is desired.  Generative Adversarial Networks (GANS): GANS consists of a generator and discriminator. The generator creates the fake data, and the discriminator distinguishes this data from the real data. A large discriminator loss classifies each example into real or fake. The points classified as fake can be considered outliers. However, this requires that the generator is trained properly on the expected data distribution.  Model Training  To train a suitable model, ensure that the dataset is balanced between normal and anomalous data. If a balanced dataset is not available, it becomes crucial to explore alternative methods for handling the imbalance.  Techniques like weighted loss can help address data imbalance if balanced data is unavailable.  Optimization of hyperparameters plays a key role in enhancing model performance by improving accuracy and reducing overfitting. Evaluation After the model has been trained, measure its performance using accuracy, precision, recall, F1, or any other relevant metric on unseen new data. This ensures that the application meets the performance standards. Refinements  After successfully evaluating the model on the test set, the next step is to deploy it for user requests. Post-deployment, continuous monitoring of the anomaly detection system is crucial.  If there are changes in the data distribution, fine-tuning a new distribution can address it. Statistical metrics such as the Kolmogorov-Smirnov test and Chi-square test can be used to detect shifts in data distribution. Explore key techniques and best practices for mastering data cleaning and preprocessing:    Anomaly Detection Challenges While the AD system can be beneficial for predicting anomalous events, some challenges remain. Let's look at some common issues when implementing an anomaly detection system.  Data Quality Poor data may involve incomplete datasets, inconsistent formats, different scales of features, duplicate data, and human error during data collection. These issues can lead to inaccurate anomaly detection, resulting in missed anomalies or excessive false positives. Training Size With limited data, the model may struggle to generalize well, leading to overfitting. In this case, the model learns the noise of the small dataset rather than generalizable features. This can result in poor performance on unseen data.  Additionally, detecting anomalies in a small dataset may cause the model to miss subtle or falsely flag normal data points as anomalous.   Imbalanced Distributions AD methods that rely on supervised learning can suffer from a class imbalance in training datasets. In practice, most data points are normal. For instance, in a disease classification task, most samples are negative, with only a small proportion being positive. As a result, the model may lack sufficient anomalous data points, causing it to become biased toward the normal class. False Positives Another issue with AD systems is false alerts, which can happen due to an incorrect confidence threshold or models that are overfitting or underfitting.  Frequent false alerts can lead users to lose trust in the system and start ignoring them. However, if a legitimate alert is missed due to this loss of trust, it could have serious consequences. {light_callout_start}} Learn how outlier detection can boost your data science workflow and improve training data: {{light_callout_end} How Encord Ensures Clean and High-quality Data for Anomaly Detection Models Developers can address the AD challenges using specialized tools for data preprocessing, validation, deployment, and refinement. Numerous open-source tools and frameworks are available; however, they may lack the advanced functionality needed to build accurate solutions for AD.  The development of AD systems can require more comprehensive third-party solutions with advanced features to address AD challenges in a single platform.  Encord is one such option.  Encord is an end-to-end data management platform for efficient data curation, labeling, and evaluation. Let’s see how Encord’s features can help you mitigate some of the challenges discussed above.  Tagging label outliers with Encord Active Key Features  Managing Data Quality and Quantity for Anomaly Detection: Encord helps you manage multimodal data, including structured and unstructured data such as audio, video, text, and images in a large quantity.  Appropriate Model Selection and Analysis: Using Encord Active, the user can assess data and AI model quality through different performance metrics to analyze its suitability for different scenarios. Encord’s Python SDK can also help develop custom monitoring pipelines.  Scalability: Encord enables you to easily scale your Anomaly Detection (AD) models by handling large datasets. The platform allows you to upload up to 10,000 data units in a single dataset, and you can organize multiple datasets to manage larger projects. It also supports uploading up to 200,000 frames per video at once for video anomaly analysis.  G2 Review Encord has a rating of 4.8/5 based on 60 reviews. Users highlight the tool’s simplicity, intuitive interface, and several annotation options as its most significant benefits.  However, they suggest a few areas for improvement, including more customization options for tool settings and faster model-assisted labeling. Overall, Encord’s ease of setup and quick return on investments make it popular among AI experts. {{light_callout_start} } Learn how to use Encord Active to enhance data quality using end-to-end data preprocessing techniques.   Anomaly Detection: Key Takeaways The effectiveness of AD systems relies on data volume, quality, and choice of the algorithm. Below are some of the key points to remember regarding the AD system: Advantages of AD: AD offers several key benefits, such as improving data quality by identifying outliers, enhancing security through fraud detection, and providing early warnings for system failures or abnormal behavior. AD Challenges: Building an accurate AD model requires addressing issues such as data quality, data volume, imbalanced data distribution, and false alerts. Encord for AD: Encord offers data cleaning, annotation, and refinement solutions to develop precise and reliable anomaly detection solutions. Get in-depth data management, visualization, search and granular curation with Encord Index.

Chatbots and virtual assistants define the current artificial intelligence (AI) landscape as users turn away from traditional channels for resolving their queries. A Gartner report predicts that search engine volume will drop 25% by 2026, with search engine marketing losing to modern AI-based mediums. The trend clearly shows the rising importance of conversational artificial intelligence as the key strategic component for an organization’s marketing efforts. However, implementing conversational AI into daily business operations is challenging due to rising data complexity and costs. In this post, we’ll discuss conversational AI in depth, covering its benefits, use cases, underlying technology, best practices for building a solution, and key challenges. We will also go over how Encord can help you create effective AI-driven conversational systems. Conversational AI: An Overview Conversational AI systems use natural language to interact with humans. Examples include AI-powered chatbots and virtual assistants like Amazon Alexa and Apple’s Siri.  It may also include other voice assistants embedded in devices like smartphones and smart speakers. Such technologies enable organizations to streamline customer interactions and boost operational efficiency. Benefits of Conversational AI Conversational AI technology offers businesses multiple advantages over traditional solutions. Benefits include: Cost Savings: It automates repetitive tasks and minimizes operational costs by handling high volumes of inquiries. It also helps optimize resource allocation, allowing businesses to focus more on strategic initiatives. Scalability: AI assistants quickly scale to handle increased workloads during peak times, such as holidays or promotional events. Unlike human agents, they can manage thousands of interactions simultaneously across multiple channels. Better Data Insights: Conversational AI platforms collect and analyze vast customer data to extract deep behavioral patterns and preferences. These insights enable businesses to improve products, services, and user experience. Better Customer Experience: AI-based virtual agents provide 24/7 customer support and identify customer needs to deliver a more personalized experience. Conversational AI Use Cases As conversational AI models continue to advance, their applications are also expanding. However, several mainstream use cases include: Healthcare: Virtual assistants can help patients with appointment scheduling, symptom checking, and medication reminders. They can also power telemedicine platforms, providing patients instant access to information and preliminary advice. The approach enhances accessibility while reducing administrative burdens on healthcare providers. Financial Services: Conversational AI applications can help customers with timely account updates, transaction details, and self-service options. They can also provide investment strategy recommendations based on predictive analysis of the stock market and economic conditions. Virtual agents can also assist banks in performing routine tasks such as filling out forms, answering straightforward queries, and resolving complaints. Contact Centers: Conversational AI automates tasks like answering FAQs, routing calls, and collecting customer feedback. It works seamlessly alongside human agents and enables faster issue resolution with 24/7 availability. The technology reduces operational costs and ensures consistent omnichannel support for better customer experience. E-Commerce: Retailers can use conversational AI chatbots on their e-commerce sites to offer customers personalized product recommendations, assist with order placement, and handle returns. Developers can integrate the chatbot with the site’s search engine to improve product discoverability and provide relevant search results based on user input. Education: Conversational AI in education includes virtual tutors and interactive learning platforms to offer students an engaging learning environment. These systems can consist of multilingual and speech recognition capabilities to increase accessibility for students worldwide. How Does Conversational AI Work? Numerous AI architectures now power conversational AI applications across the abovementioned use cases. However, these frameworks primarily rely on three core components to facilitate user interactions. Natural Language Processing (NLP) NLP algorithms use techniques like tokenization, named entity recognition (NER), and sentiment analysis to understand human language. It can include breaking down user inputs into structured data, identifying linguistic patterns, and interpreting meaning. Embeddings Modern NLP methods convert text into word embeddings. These are vectorized representations of textual datasets. These embeddings allow AI models to analyze complex linguistic patterns, grammatical structures, and sentence variations. Understanding User Intent Once AI models process language through NLP techniques, the next component relates to natural language understanding (NLU). NLU analyzes a user’s intent to determine the most optimal response. For example, a particular phrase may have two meanings based on different contexts. NLU identifies this context to get the phrase’s relevant meaning. Word embeddings are critical in modern NLU methods. Conversation AI models use statistical techniques to match a user’s query with relevant background information. Vector Similarity Search: The technique calculates the distance between the knowledge base or data vectors and query vectors to measure similarity For example, the model computes a similarity metric between the query’s embeddings and the embeddings of a knowledge base. Embeddings with the highest similarity provide the model with the relevant context. Generative AI (GenAI) Once the model understands the context, the next step is to generate a naturally-sounding, context-specific response. Developers often integrate chatbots and virtual assistants with deep learning, including large language models (LLMs), to generate responses. While vanilla Gen AI models typically support textual data, they can also include text-to-speech (TTS) or speech-to-text (STT) frameworks. TTS models transform text-based responses into speech, while STT architectures convert spoken language into text. Also, the latest GenAI solutions are becoming multimodal. They now support text, audio, and image data simultaneously. Learn how to build a generative AI evaluation framework with Encord Building Conversational AI: Best Practices The steps to build a conversational AI system can vary significantly based on the specific use case and domain. However, the following guidelines provide a starting point for developing a conversational AI solution. 1. Identify FAQs Start by compiling a comprehensive list of frequently asked questions (FAQs) relevant to your use case. Analyze customer interactions, support tickets, and feedback to identify common queries. Categorize them by topic to ensure a structured approach. The technique helps create a robust knowledge base and allows your conversational AI system to respond accurately to user inquiries. 2. Establish Conversational AI’s Goals based on FAQs Analyzing FAQs and understanding user needs will help you define clear objectives for your conversational AI system. Identify the key problems the AI should address, such as resolving customer queries, automating repetitive tasks, or providing recommendations. You must also train your model to handle the same query differently. For instance, a user who wants to subscribe to your services may ask, “How to subscribe?” Another user, however, may ask, “Where to sign up?” Your system must cater to such variations. Align these goals with business priorities to ensure the system delivers value, improves user experiences, and meets specific use case requirements. 3. Identify Common Entities Recognize and define the key entities relevant to your conversational AI use case, such as names, dates, locations, or product details. These entities help the system extract critical information from user inputs. Use domain-specific data and NLP tools to identify and tag these entities accurately. This will ensure precise understanding and context-driven responses in conversations. 4. Design for Intuitive Conversations Ensure your conversational AI facilitates natural, user-friendly interactions. Use clear, concise language and anticipate user needs to guide conversations effectively. Incorporate context retention, error handling, and fallback mechanisms for seamless experiences. Design flows that mimic human conversations to provide logical responses and smooth transitions. The process helps users quickly achieve their goals without confusion or frustration. 5. Simplify the Interface Create a user-friendly interface that minimizes complexity and enhances accessibility. Use straightforward designs with intuitive navigation. Provide clear prompts, buttons, or menus for common actions to reduce reliance on typing. A streamlined interface improves user experience and increases adoption of your conversational AI system. 6. Implement Reinforcement Learning (RL) Incorporate RL to improve your conversational AI system over time. Train the model using real-world interactions and reward it for accurate and helpful responses.  Reinforcement Learning: The LLM uses a reward model to adjust its outputs based on human feedback This approach helps the AI adapt to user preferences and ensures the system evolves to meet changing user needs. 7. Prioritize Data Privacy and Security Ensure your conversational AI system complies with data protection regulations and industry standards for data privacy. Implement encryption, secure storage, and access controls to protect user data. Minimize data collection to only what is necessary and provide transparency about usage. Regularly audit and update security measures to build trust and protect sensitive information during interactions. 8. Optimize for Multilingual Support and Accessibility Design your conversational AI to support multiple languages, enabling seamless interaction for a diverse user base. Implement language detection and translation features to ensure inclusivity. Ensure the system adheres to accessibility standards to accommodate diverse user needs. This makes it user-friendly for individuals with disabilities or varying levels of technical proficiency. 9. Integrate with Multiple Channels Enable your conversational AI to operate seamlessly across various channels, such as websites, mobile apps, social media platforms, and messaging apps. Ensure consistent user experiences by synchronizing conversations across these platforms. Multi-channel integration broadens your AI’s reach, enhances accessibility, and allows users to interact through their preferred communication medium. 10. Establish Robust Monitoring Systems Implement monitoring systems to track conversational AI’s performance. Use analytics to evaluate metrics like response accuracy, user satisfaction, and engagement rates. Review logs regularly for errors or unusual patterns. Proactive monitoring enables you to identify issues, optimize performance, and ensure the system meets user expectations. Learn how multiagent systems can improve your AI frameworks Challenges of Building Conversational AI While the above guidelines provide a strong foundation, developers may encounter several challenges when building conversational AI systems. The following list outlines some of the issues they might face. Language Data Complexity and Size: Collecting and curating large, diverse, and accurate language data can be time-consuming and expensive. Handling noisy, ambiguous, or low-resource languages adds more complexity and affects model performance. Scaling Conversational AI Models: As AI models grow, scaling them to handle increased user interactions and data volumes becomes challenging. Ensuring consistent performance across millions of users, maintaining low latency, and optimizing resource usage requires sophisticated infrastructure and computational power. Integrability: Building conversational AI systems that seamlessly integrate with existing platforms, APIs, and third-party services can be complex. Ensuring smooth communication between systems while maintaining data consistency and reliability adds to the integration challenge. Security: Protecting sensitive user data from breaches, ensuring compliance with privacy regulations, and mitigating risks like data misuse or unauthorized access are critical. Security vulnerabilities in conversational AI systems can compromise user trust and lead to costly legal and reputational damages. Encord for Conversational AI Addressing the challenges outlined above often demands extensive domain expertise and technical proficiency. Organizations can use specialized third-party solutions like Encord to simplify the creation of high-performing AI models. Encord is an end-to-end AI-based data management platform that lets you create, curate, and annotate large-scale conversational AI datasets. It offers the latest annotation features for multiple NLP tasks and enables you to automate curation workflows through state-of-the-art (SOTA) models. Encord Key Features Create and Curate Large Datasets: Encord helps you develop, curate, and explore extensive textual datasets through metadata-based granular filtering and natural language search features. It can also extract text from multiple document types and organize them according to their contents. Text Annotation: The platform lets you annotate and classify text with Encord agents, allowing you to customize labeling workflows according to your use case. It supports text classification, NER, PDF text extraction, sentiment analysis, question-answering, and translation. Scalability: Encord can help you scale conversational AI models by ingesting extensive multimodal datasets. For instance, the platform allows you to upload up to 10,000 data units simultaneously as a single dataset. You can create multiple datasets to manage larger projects and upload up to 200,000 frames per video at a time. Data Security: The platform adheres to globally recognized regulatory frameworks, such as the General Data Protection Regulation (GDPR), System and Organization Controls 2 (SOC 2 Type 1), AICPA SOC, and Health Insurance Portability and Accountability Act (HIPAA) standards. It also ensures data privacy using robust encryption protocols. Integration: Encord supports integration with mainstream cloud storage platforms such as AWS, Microsoft Azure, and Google Cloud. Using its Python SDK, you can also programmatically control workflows. G2 Review Encord has a rating of 4.8/5 based on 60 reviews. Users highlight the tool’s simplicity, intuitive interface, and several annotation options as its most significant benefits.  However, they suggest a few areas for improvement, including more customization options for tool settings and faster model-assisted labeling. Overall, Encord’s ease of setup and quick return on investments make it popular among AI experts. Conversational AI: Key Takeaways As users shift toward AI-based mediums to resolve their queries, the need for conversational AI will increase to enhance customer satisfaction. Below are a few key points to remember regarding conversational AI. Conversational AI Benefits: Businesses can use conversational AI tools like chatbots and virtual agents to improve customer experience, save costs, scale operations, and extract data-based insights for strategic decision-making. Conversational AI Challenges: Large and diverse language datasets, scalability constraints, limited integrability, and security concerns make it difficult to develop conversational AI models. Encord for Conversational AI: Encord’s text extraction and annotation features can help you manage complex language datasets and build enterprise-grade conversational AI systems. If you're extracting images and text from PDFs to build a dataset for your multimodal AI model, be sure to explore Encord's Document Annotation Tool—to train and fine-tune high-performing NLP Models and LLMs.

Last month, Encord was one of a number of global tech companies invited by Amazon Web Services (AWS) to attend the event dubbed Project Stormcloud. In launching Project Stormcloud, The Royal Navy’s Office of the Chief Technology Officer challenged global technology giants Microsoft and AWS to demonstrate how companies could bring new, state-of-the-art cloud-based technology into the defence industry. As part of the Stormcloud Community, we’ve been supporting the Royal Navy and British Defence by providing critical computer vision infrastructure for the project, enabling the defence industry to automate visual tasks, annotate data for internal intelligence analysis, and store data at a large scale. This support allows for the application of AI for an instant real-time, on-the-ground intelligence picture. Being involved in Project Stormcloud has been a great experience for us as a company. It has been a privilege to be part of this consortium of innovators. We got to work and integrate with some of the leading tech companies in the government sector. The fact that the UK tech ecosystem could achieve so much in such a short period of time really speaks volumes about its quality. We also gained a lot of insight into the importance of using the right data to achieve specific mission objectives. It was useful to learn how our applications can be used to achieve real-time situation awareness. Stormcloud, with AWS, Microsoft, and their range of partners, will progress further over the next year to incorporate ideas from across Defence and to demonstrate how two of the leading global tech companies can revolutionize how to get technology into the hands of sailors and Royal Marines.  We look forward to continuing to be part of the journey. ‍ Ready to automate and improve the quality of your data labeling?  Sign-up for an Encord Free Trial: The Active Learning Platform for Computer Vision, used by the world’s leading computer vision teams.  AI-assisted labeling, model training & diagnostics, find & fix dataset errors and biases, all in one collaborative active learning platform, to get to production AI faster. Try Encord for Free Today.  Want to stay updated? Follow us on Twitter and LinkedIn for more content on computer vision, training data, and active learning.

What is Natural Language Search? Natural Language Search (NLS) is a type of search interface that uses Artificial Intelligence (AI) and allows users to query data in natural language rather than using structured queries like SQL, keywords or specific query syntax. NLS relies on Natural Language Processing (NLP) techniques to interpret and understand user queries, extracting meaning, context, and intent from it so that the system can provide accurate and relevant results. NLS harnesses the power of NLP to translate a user’s natural-language input into the structured commands or data filters needed to retrieve information from a database or search index. NLS is designed to simplify interactions with databases, search engines, or other information systems by making it more accessible and intuitive for non-technical users. Example of Natural Language Search For example, imagine you're planning a trip and want to find a hotel with specific amenities. Using a traditional keyword-based search, you might input: "Hotel pool gym free Wi-Fi" This search could yield results that include any or all of these terms, but it may not accurately capture your specific requirements. With Natural Language Search, you can enter a more conversational query: "Find hotels with a pool, gym, and free Wi-Fi near me" The NLS system processes this query by understanding the context and intent behind your words. It recognizes that you're looking for hotels offering specific amenities in your vicinity. By interpreting the natural language, the system can provide more accurate and relevant search results that match your criteria. This approach enhances the user experience by allowing searches to be conducted in a more natural and conversational manner which reduces the need for users to formulate precise keyword combinations and also gives better results. Keyword Search vs Natural Language Search NLS and Keyword Search are two different approaches used in information retrieval. Each search is different in how it interprets user queries and delivers results. Keyword Search Keyword search relies on matching specific words or phrases entered by the user to indexed content. The user input needs to be concise and only targeted keywords must be used to get best results. The results depend on exact keyword matches as it may return irrelevant results if the exact terms aren't present. Following is the example of keyword search: User Query: "best Italian restaurants NYC" Interpretation: Searches for documents containing the exact phrases "best," "Italian," "restaurants," and "NYC." Natural Language Search (NLS) NLS uses NLP to understand the intent and context behind a user's query to enable more conversational and intuitive searches. User input can be full sentences or questions that are similar to natural human communication. The results are based on the interpreted meaning and it may give better results even if exact keywords are not present. Following is the example of NLS: User Query: "Where can I find the best Italian restaurants in New York City?" Interpretation: Understands the user's intent to locate top-rated Italian dining options in NYC, considering synonyms and contextual relevance. Keyword Search vs NLS  Let us the differences between NLS and Keyword Search with more detailed explanations, examples, and insights into how they function. Input Style and User Experience In keyword search input requires specific terms or phrases to be searched. Users must guess or anticipate the exact keywords likely to be present in the indexed content. If the user does not know the exact terms, results may be irrelevant or incomplete. Consider the example below.   NLS allows users to specify full, conversational sentences or questions. It is designed to mimic how people naturally ask questions. Users don’t need to think about the exact wording, as the system interprets the intent of the query. For example:   Processing and Understanding Keyword search uses basic string matching or pattern recognition to locate results. It lacks the ability to understand relationships between words or interpret intent behind query. It sometimes struggles with synonyms or variations of phrases. For example, it treats “NYC” and “New York City” as different entities. For example:   NLS uses NLP, which breaks down and analyzes queries to identify key entities (e.g., “Italian restaurants” and “NYC”) and also understand the query intent (e.g., finding dining options). It also uses synonyms and alternate phrasing and recognizes relationships between words and responds to implied meanings. For example:   Relevance of Results Keyword based search matches results based on the presence of keywords in the search index. As a result it may return irrelevant results if keywords are vague or used in unrelated contexts. It also struggles to prioritize results based on the query’s implied importance. For example:   NLS interprets the user’s intent and retrieves results that align with the overall meaning, not just word matches. It understands implied context, such as “best” indicating user interest in recommendations and also ranks results based on semantic relevance and quality. For example:    Handling Complex Queries Keyword search works well for short queries but struggles with complex queries that involve relationships between multiple concepts. For example:   NLS excels at complex, multi-faceted queries by understanding relationships between criteria (e.g., hotels, free Wi-Fi, pool, beach, California). It filters and ranks results to prioritize user preferences. For example:    Keyword Search has been foundational in information retrieval. However, Natural Language Search offers a more intuitive and user-friendly experience by understanding the intent and context of user queries. This leads to more accurate and relevant search results, enhancing overall user satisfaction. How Does Natural Language Search Work? Following are the steps involved in NLS  which enables the search system to interpret and respond to user queries phrased in everyday language. Query Analysis and Intent Recognition This step involves understanding what the user wants to achieve with their search. It not only considers the words but also seeks to understand the underlying purpose or goal of the query. For example:   Entity Recognition In this step, the specific piece of information (entities) is identified within the query, such as names of people, places, dates, or products. This helps in focusing on exactly what the user is referring to. For example:   Entity Recognition (Source) Semantic Understanding and Context Interpretation In this step the meaning behind the words in the query is understood by considering context, word relationships, and nuances. This ensures that the system understands the query as a whole, rather than just individual words. For example:    Query Expansion This step involves enhancing the original query by adding related terms or synonyms to improve search results. This helps in retrieving information that might be relevant but expressed differently. For example:    Information Retrieval In this step, the databases or indexes are searched to find content that matches the query of the user. This is where the system gathers potential answers or relevant information. For example:    Example of Information Retrieval Ranking and Relevance Scoring This step involves evaluating and ordering the retrieved information based on how well it matches the query of the user as well as his intent. Higher relevance scores indicate more pertinent results, which are then presented first. For example:    Presentation of Results In this step the ranked information to the user is displayed in a clear and accessible manner with summaries, images, or direct answers to enhance user experience. For example:    AI based search result presentation  Continuous Learning and Feedback Integration This step is responsible for providing feedback to improve future search accuracy and relevance by learning from user interactions. This involves updating algorithms based on what users find helpful or unhelpful. For example:    By understanding and implementing these components, NLS systems can effectively interpret user queries and provide accurate, contextually relevant results and enhance the overall search experience. Application of Natural Language Search There are many applications of NLS systems in various domains. Following are some of the examples of how NLS systems redefine search in these domains. E-commerce Platforms NLS allows customers to search for products using natural language queries which provides desired results and improves user’s the shopping experience. For example, a user can type "comfortable running shoes under $100" and receive relevant product suggestions. Product Search (Source) Virtual Assistants and Chatbots NLS enables virtual assistants to understand and respond to user queries conversationally. For example asking Siri or Alexa, "What's the weather like today?" prompts a weather update. Alexa - a virtual assistant Healthcare Information Systems NLS assists healthcare professionals in retrieving patient information or medical records using natural language queries. For example, a doctor can query, "Show me the latest lab results for John Doe." and the system comes up with the specific records. Educational Platforms Students can use NLS to find study materials or answers to academic questions. For example, typing "explain the theory of relativity" yields educational resources on the topic. Customer Support Services NLS enhances customer service by allowing users to describe issues in their own words which provides efficient problem resolution. For example, a user can state, "I'm having trouble logging into my account," and receive targeted assistance. Content Management Systems NLS helps users locate documents or media files within large databases using natural language queries. For example, a user may ask "find the latest marketing presentation"  to the search system and retrieve the relevant file. Search Engines NLS improves search engines by interpreting user intent behind complex queries thus providing better and relevant search results. For example, entering the query "best places to visit in Europe in spring", the NLS search engine provides best travel recommendations. Bing Search Engine The Role of AI in Transforming Search AI has transformed search technology and the way users interact with information retrieval systems. With the help of advanced machine learning, natural language processing, and data analysis techniques, AI enhances the ability of search engines to understand, interpret, and provide accurate results.  NLS to search multimodal files in Encord (Source) Understanding Natural Language Queries Traditional search relies on matching keywords in queries with indexed content which often leads to irrelevant results for vague or complex queries. AI-powered search uses NLP to understand the intent and context behind queries. It allows users to search in conversational language which makes the process more natural. Personalization Traditional search provided generic results with limited customization. AI based search analyzes user behavior, preferences, and past interactions to personalize search results. Factors such as location, search history, and device type are used to enhance responses. Semantic Search Traditional search focused on exact keyword matching which sometimes missed the context. AI based search understands the meaning behind words and their relationships within a query. Synonyms, paraphrases, and context are considered to provide more relevant results. Visual Search Traditional search relied on text-based queries only. Use of computer Vision enables users to search using images also. AI analyzes visual content, recognizes objects, and provides information or matches. Voice Search Traditional search required users to type queries manually. NLP powers voice recognition systems which allows users to ask questions using voice commands. AI converts spoken language into text, processes it, and provides responses. Conversational Search Traditional search offered static, one-time results. Conversational AI enables ongoing, interactive dialogues, refining search results in real time. Users can ask follow-up questions without rephrasing or starting over. Multimodal Search Traditional search was limited to single-mode inputs (e.g., text only). AI supports multimodal search, combining text, images, and voice inputs for more dynamic queries. AI-Generated Summaries and Answers In the results from traditional search users are required to sift through links to find answers. AI based search generates concise summaries or direct answers to user queries using Generative AI models and also provides links to resources. AI is transforming search into an intelligent, personalized, and context-aware experience. By integrating NLP, AI based search systems provide accurate and meaningful search results. This shift is redefining how we access and interact with information across industries to enhance productivity and satisfaction. How Encord helps build or fine-tune search models Encord is a powerful data annotation platform that plays a vital role in building and fine-tuning search models especially for NLS systems. By providing tools for creating high-quality NLP datasets, Encord ensures search systems are accurate, efficient, and contextually aware. Comprehensive Document and Text Annotation Tools Encord offers tools that support text annotation tasks such as sentiment analysis, question answering, and translation to accurately label documents and text. By creating accurately labeled datasets, search models can better understand and process natural language queries which provide more accurate and relevant search results. Integration of State-of-the-Art Models Encord allows the integration of advanced models like GPT-4o and Gemini Pro 1.5 into data workflows to automate and accelerate the annotation process. Using these models enhances the quality and consistency of annotations and provides a solid foundation for training search algorithms capable of understanding complex queries. Customize multimodal data workflows in Encord Multimodal Data Management: Encord enables the annotation of multimodal data types such as text, images, and documents, within a single platform. This capability is crucial for developing search models that need to process and retrieve information across different data formats, ensuring comprehensive search functionalities. Annotating of document, image and video in Encord Customizable Annotation Workflows: Encord provides customizable workflows and quality control tools which helps in customizable annotation processes that meet specific project requirements. Customized annotation workflows ensure that the training data aligns closely with the intended use cases of the search model. This improves the performance of search models and their relevance in real-world applications. Fine-Tuning Foundation Models: Encord offers resources and tools to fine-tune foundation models, such as Meta AI's Segment Anything Model (SAM), to specific applications. Fine-tuning these models with domain-specific data enhances their ability to understand and process specialized queries which leads to more precise and effective search outcomes. The NLP data annotation capabilities offered by Encord enables development and refining search models that are more accurate, context-aware, and responsive to user queries which as a result helps in enhancing the overall search experience provided by NLS search engines. Key Takeaways: Natural Language Search  NLS allows users to interact with search systems in conversational language. NLSuses  NLP to understand user intent and context and offer more accurate and relevant results compared to traditional keyword-based searches. Keyword searches rely on exact matches and may return irrelevant results if terms don’t align perfectly. NLS, on the other hand, interprets user intent and considers synonyms, context, and relationships between words to provide meaningful results. NLS simplifies complex queries by understanding relationships between multiple criteria and delivering precise results. AI has revolutionized search systems by enabling features like semantic understanding, voice and visual search, personalization, and multimodal search. It ensures results are meaningful, context-aware, and tailored to individual user needs. NLS is widely used in e-commerce, virtual assistants, healthcare, education, customer support, and content management. It allows users to interact naturally and improve search accuracy and relevance. Encord facilitates the development of NLS systems by providing robust annotation tools, multimodal data management, and customizable workflows. It enables the creation of high-quality datasets and fine-tuning of foundation models to build contextually aware and highly responsive search systems. If you're extracting images and text from PDFs to build a dataset for your multimodal AI model, be sure to explore Encord's Document Annotation Tool—to train and fine-tune high-performing NLP Models and LLMs.

Machine learning depends on well-structured, high quality data. At the very core of this process is data classification when building models with supervised learning. It is organizing raw information into labeled and organized categories that the AI models can understand and learn from. In this guide, we will discuss the fundamentals of data classification and how it impacts artificial intelligence applications. What is Data Classification? It is the process of organizing unstructured data into predefined categories or labels. This process is carried out after data curation, where data is carefully collected from various sources. Data classification is a foundational step in supervised machine learning, where models are trained on labeled datasets to make predictions or identify patterns. Without accurate data classification, machine learning models risk producing unreliable or irrelevant outputs. Supervised Machine Learning Why is Data Classification Important? Data classification determines the quality of the training and testing data. This determines the quality of the machine learning model you are building. The models rely on well annotated data to: Learn patterns: Recognize correlations between the input and labels. Make predictions: Apply the patterns learnt to new, unseen data. Reduce noise: Filter out irrelevant or redundant information to improve accuracy in predictions. Types of Data Classification Data classification can be applied to various types of data: Text: Categorizing documents, emails, or social media posts. Images: Labeling objects, scenes, or features in visual data. Audio: Identifying speakers, transcribing speech, or classifying sounds. Video: Detecting and labeling activities or objects in motion. Steps in Data Classification To classify data effectively, you need to design a structured process to ensure that the data created is comprehensive, and ready for the next step, i.e., feature engineering or training the AI model. Here are the key steps to include in the data classification process: Data Collection The collection of high-quality and relevant data forms the foundation of the data classification process. The goal is to build a dataset that is both representative of the problem domain and robust enough to handle edge cases. When collecting data, you need to  keep these points in mind: Diversity: Ensure your dataset includes various scenarios, demographics, or use cases to avoid bias. For example, a facial recognition dataset should include diverse skin tones and facial features. Relevance: Align your data with the problem you’re solving. Irrelevant or extraneous data can introduce noise and hinder model performance. Volume: While more data is generally better, focus on quality. A smaller, well-annotated dataset can outperform a massive dataset filled with noisy samples Data Labeling This process converts raw, unstructured data into usable training examples. Here you assign meaningful labels or annotations to data samples, making them understandable for machine learning algorithms. Data labeling also helps the team to analyse the quality of the curated dataset. This helps them decide whether or not more data should be collected or if the collected dataset is suitable for the project. Here are some of the steps involved in data annotation: Manual Annotation: Human annotators label data by identifying patterns or tagging content, such as marking objects in images or identifying sentiment in text. This can be highly accurate but time-intensive. There is a certain amount of time also spent in training the annotators and designing an annotation schema to ensure the quality of the annotation. Automated Labeling: Pre-trained models or annotation tools like Encord generate initial labels. These can then be verified or refined by humans to ensure quality. When annotating a large volumes of data, this automation can reduce the time spent significantly, but human intervention is required regularly to ensure the quality of the annotation. Consensus Mechanisms: Involving multiple annotators for the same data point to resolve ambiguities and improve consistency. Though the time spent here is considerably more, it is essential when building a robust training dataset for high impact projects like AI models in the medical field. Feature Engineering Feature engineering extracts meaningful information from the annotated data. The features extracted from the annotated data are extracted in a way to help the ML model understand the data and learn from it. Feature engineering involves: Identifying Features: Determine which attributes of the data are most relevant for classification. For example, in text classification, word frequencies or bigrams might be useful. Transforming Data: Normalize or preprocess the data to make it consistent. For images, this might involve resizing or enhancing contrast. Reducing Dimensionality: Remove irrelevant or redundant features to simplify the dataset and improve model efficiency. Model Training and Testing Once labeled and features are extracted, the data is split into training, validation, and testing sets. Each set serves a specific purpose: Training Set: This dataset is initially used by the model to learn the patterns in the data. Validation Set: This unseen set helps tune model parameters after it has been trained on the training dataset to avoid overfitting. Testing Set: In this stage, the model’s performance is evaluated on unseen and close to real-world dataset and used to generalise the model’s responses. Continuous Improvement The process doesn’t stop after initial training. Data classification models often need: Retraining: Incorporating new data to keep models up to date. Error Analysis: Reviewing misclassified examples to identify patterns and refine the process. Active Learning: Allowing models to request labels for uncertain or ambiguous cases, which can help focus human labeling efforts. By continually iterating on these steps, you ensure your data classification remains accurate and effective over time. Challenges in Data Classification Despite its importance, the data classification system is not without its challenges. You will encounter: Inconsistent Labels Human annotators may interpret data differently, leading to inconsistent labeling. For example, in sentiment analysis, one annotator might label a review as “neutral” while another marks it as “positive.” These discrepancies can confuse machine learning models and reduce accuracy. Solution Establish clear annotation guidelines and use consensus mechanisms. Tools like Encord’s annotation platform allow multiple reviewers to collaborate, ensuring labels are consistent and aligned with project objectives. Dataset Bias A biased dataset leads to models that perform poorly on underrepresented groups. For instance, a facial recognition system trained on a dataset with limited diversity may fail to identify individuals from minority demographics accurately. Solution Incorporate diverse data sources during the collection phase and perform bias audits. Using data quality metrics to analyse the annotated dataset helps in identifying the underrepresented data groups which are necessary for building a robust deep learning model. It is also essential to keep in mind that some projects need certain groups in small amounts and need not be overpopulated, otherwise the model may learn patterns which are not necessary for the project. Hence, the data quality metrics are essential to be analysed to ensure necessary groups are represented as requirements. Scalability Issues Manually labeling large amounts of data can be time-consuming and expensive, especially for high-volume projects like video annotation.  Solution Using a scalable platform that can handle different modalities is essential. The annotation platform that provides automated labelling features helps speed up the process while maintaining accuracy. Quality Control Ensuring label accuracy across large datasets is challenging. Even small errors can degrade model performance. Also, migrating data from the annotation platform, and designing and implementing your own data evaluation metrics is time consuming and not very scalable. Solution Use a data platform that stores different annotated datasets and provides quality metrics to visualize and analyze the quality of the data. This quality control should include label validation and auditing annotation workflows while assessing the quality of the curated dataset. How Encord Streamlines Data Classification Encord provides a comprehensive suite of tools designed to optimize every stage of the data classification process. Here’s how it addresses common challenges and accelerates data classification algorithms: Intuitive Annotation Platform Encord Annotate’s interface supports diverse data types, including images, videos, and audio in various formats. Its user-friendly design ensures that annotators can work efficiently while maintaining high accuracy. The ontologies or the custom data annotation schema ensures precision in the annotated data. You can also design annotation workflows to simplify the process.  Encord Annotate in action Accelerate labeling projects and build production-ready models faster with Encord Annotate. Automation with Human Oversight Encord combines automated labeling with human review, allowing teams to label large datasets faster without sacrificing quality. For example: Pre-trained models generate initial labels. Human reviewers validate and refine these labels. Collaboration and Consensus With built-in collaboration tools, Encord enables teams to work together seamlessly. Features like comment threads and real-time updates improve communication and ensure consensus on labeling decisions. Quality Assurance Tools Encord’s quality control features include: Inter-annotator Agreement Metrics: Measure consistency across annotators. Audit Trails: Track changes and identify errors in labeling workflows. Validation Workflows: Automate error detection and correction. Analytics and Insights Encord provides actionable insights into dataset composition, annotation progress, and model readiness. These analytics help teams identify bottlenecks and optimize workflows for faster time-to-market. By addressing these challenges, Encord empowers teams to build high-quality datasets that accelerate machine learning development and reduce labeling errors. Evaluating the Impact of Effective Data Classification When done correctly, data classification leads to better model performance, faster development cycles, and real-world applicability. By using platforms like Encord to streamline the classification process, organizations can focus on deploying AI systems that drive tangible outcomes. Here are the key benefits: Improved Model Accuracy When data is properly classified, machine learning models can learn from clear and consistent patterns in the training data. This reduces noise and ambiguity, allowing the models to make more accurate predictions. For example, in applications like fraud detection or medical diagnostics, precise labeling ensures that the model correctly identifies anomalies or critical conditions. This not only improves precision and recall but also minimizes errors in high-stakes environments where accuracy is paramount. Enhanced Generalization for Models Accurate classification ensures that datasets are diverse and balanced, which directly impacts a model’s ability to generalize to new data. For example, a facial recognition model trained on a well-classified dataset that includes various skin tones, age groups, and lighting conditions will perform reliably across different scenarios. Streamlined Decision-Making Properly classified data provides a solid foundation for drawing actionable insights. Clean and organized datasets make it easier to analyze trends, identify patterns, and make data-driven decisions. In industries like finance or retail, this can mean quicker identification of fraud, improved inventory management, or a better understanding of customer behavior.  Regulatory Compliance and Data Security In regulated industries like healthcare and finance, proper data classification is essential for meeting compliance standards such as GDPR, HIPAA, or PCI-DSS. Classifying sensitive information correctly ensures that it is stored, accessed, and processed in line with regulatory requirements according to data protection laws. Also, classification helps in cybersecurity as you segregate sensitive data from less critical information, improving overall security and reducing the risk of data breaches. Laying the Foundation for Active Learning Effective data classification supports iterative improvements in machine learning models through active learning. In this process, models can request additional labels for ambiguous or uncertain cases, ensuring that they are trained on the most relevant examples. This approach not only enhances long-term accuracy but also focuses human labeling efforts where they are most needed, optimizing both time and resources. Key Takeaways: Data Classification Data classification organizes raw data into labeled datasets essential for training machine learning models. Accurate, diverse, and relevant data ensures better model performance and generalization. Automated tools like Encord speed up labeling while maintaining quality through human oversight. Clear guidelines, bias audits, and validation workflows address issues like inconsistent labels and dataset bias. Regular retraining, error analysis, and active learning keep models accurate and effective. Effective classification improves decision-making, supports compliance, and enhances data security. Data classification is more than just a preparatory step; it’s the foundation of any successful machine learning project. With the growing demand for AI algorithms, the need for efficient, accurate, and scalable classification workflows is higher than ever. Data management and annotation platforms like Encord simplify this process, offering powerful classification tools to reduce errors, improve quality, and speed up development. 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AI has come a long way in terms of how we interact with it. However, even the most advanced large language models (LLMs) have their limitations - ranging from outdated knowledge to hallucinations. Enter Retrieval Augmented Generation (RAG) pipelines - a method to build more reliable AI systems. RAG bridges the gap between generative AI and real-world knowledge by combining two powerful elements: retrieval and generation. This helps the models to fetch relevant and up-to-date information from external sources and integrate it to their outputs.  Whether it’s answering real-time queries or improving decision-making, RAG pipelines are quickly becoming essential for building intelligent AI applications like chatbots. This guide explores RAG pipelines, starting from its fundamentals to implementation details. By the end, you’ll have a clear understanding of how to develop smarter AI models using RAG and how Encord can help you get there. What are Retrieval Augmented Generation (RAG) Pipelines? RAG pipelines combine information retrieval with language generation to build reliable and adaptable AI systems. Unlike traditional LLMs which solely rely on pretraining, RAG pipeline improves the LLM’s generative capabilities by integrating real-time information from external sources. Source How RAG Works? The pipeline operates in two main stages:   Retrieval: The model sources relevant data from an external knowledge base. Generation: The retrieved data is then used as context to generate responses. Key Components of RAG Pipelines Knowledge Retrieval System: This could be a vector database like FAISS, Pinecone or a search engine designed to find the most relevant data based on user queries. Generative Model: Models like OpenAI’s GPT-4, Anthropic’s Claude Sonnet, or Meta AI’s Llama 3 or open-source models like Google’s Gemini are used to generate human-like responses by combining the retrieved knowledge with the user’s input. Why does RAG Matter? Traditional LLMs struggle with outdated or irrelevant information, leading to unreliable outputs. RAG solves this by enabling models to: Incorporate real-time knowledge for dynamic applications like news reporting or customer support. Curate responses based on domain-specific knowledge, improving accuracy for niche industries like legal, healthcare, or finance. Benefits of RAG Applications Better Accuracy: By using external knowledge bases, it reduces the chances of hallucinations and inaccuracies. Scalability for Domain specific Applications: These pipelines make the LLMs adaptable to any industries. It depends on the type of knowledge base used. From generating legal opinions based on cases to helping in medical research, RAG can be tailored to meet the needs of specific use cases. Easy to Adapt: RAG pipelines can easily integrate with various knowledge sources, including private datasets, public APIs, and even unstructured data, allowing organizations to adapt to changing requirements without retraining their models.  Cost Efficient:  Rather than retraining an entire model, RAG pipelines rely on data retrieval to access external data. This reduces the need for expensive compute resources and shortens the development cycle. Alternatives to RAG Systems Other than RAG, there are other methods used to improve LLM’s outputs. Here are a few methods that are commonly used and how RAG compares to them. RAG vs Fine Tuning In fine-tuning, the LLM’s parameters are retrained with curated training data, to create a model tailored to a specific domain or task. However, this requires significant computational resources and does not adapt to new information without further retraining. Source RAG vs Semantic Search Here, in semantic search, relevant documents or information is retrieved based on the contextual meaning or the query. But this information is not used further to generate new content. Whereas, RAG retrieves and uses it to generate informative and contextual outputs. RAG vs Prompt Engineering The prompt inputs are planned and designed in order to get desired responses from LLMs without any changes to the model. This method may work well if you are using large-scale LLMs trained on huge training datasets, still there are higher chances of inaccuracies or hallucinated information. RAG vs Pretraining Pretraining equips a model with general-purpose factual knowledge. While effective for broad tasks, pretrained models can fail in dynamic or rapidly changing domains. Whereas, RAG models have dynamic updates and provide contextually relevant responses. Building Blocks of RAG Pipelines RAG pipelines rely on two interconnected components: retrieval and generation. Each stage has specific responsibilities, tools, and design considerations that ensure the pipeline delivers accurate results. Stage 1: Retrieval The information retrieval stage is the backbone of the RAG pipeline, responsible for sourcing relevant information from external data sources or databases. The retrieved information serves as the contextual input for the generative model. Key Process Understanding Query: The input query is encoded into a vector representation. This vector is then used for semantic matching with stored knowledge embeddings. Knowledge Retrieval: The relevant data is fetched by comparing the query vector with precomputed embeddings in a vector database or search index. Methodologies and Tools Used Vector Databases: Tools like FAISS, Pinecone, and Weaviate store and retrieve high-dimensional vector embeddings. These systems enable fast, scalable similarity searches, which are useful for handling large datasets. Search Engines: ElasticSearch or OpenSearch are commonly used for text based retrieval in indexed databases. These search engines prioritize relevance and speed. APIs and External Sources: Integrations with external APIs or proprietary knowledge bases allow dynamic retrieval of information (e.g., live data feeds for news or weather). Design Considerations Dataset Quality: Retrieval systems are only as effective as the quality of the datasets they work with. High-quality, annotated data ensures the retrieval process delivers contextually accurate results. Indexing Efficiency: Properly structured indexing reduces latency, especially in applications requiring real-time responses. Domain-Specific Embeddings: Using embeddings tailored to the domain improves data retrieval precision. Key Challenges in Retrieval Here are some of the key challenges in the retrieval stage of the RAG pipeline: Ambiguity in user queries may lead to irrelevant or incomplete data retrievals. Stale data in static data sources can affect the accuracy of outputs. It is essential to choose a well maintained database. Managing retrieval latency while ensuring relevance remains a technical hurdle. This can be managed by using search engines matching the need of the project. Stage 2: Generation The generation stage uses the retrieved data from the earlier stage to generate contextually aligned responses. This stage integrates user input with retrieved knowledge to improve the generative capabilities of the model. Key Process Input Processing: The generative model takes both the user query and retrieved data as inputs, and uses them to generate coherent outputs. Response Generation: LLMs like GPT-4o or Llama 3 process the inputs and generate text responses tailored to the query. Methodologies and Tools Used Generative Models: Pretrained models serve as the foundation for generating human-like text. The retrieved data provides additional context for improved relevance. Prompt Engineering: The prompt inputs are designed to ensure that the retrieved knowledge is appropriately incorporated into the output response. Key Challenges in Generation Merging the retrieved information with user queries can result in overly verbose or irrelevant outputs. Handling cases where no relevant data is retrieved requires fallback mechanisms to maintain trust in the responses. Building Effective RAG Systems  RAG systems rely on high quality data curation, efficient embedding storage, and a reliable data retrieval system to generate relevant output. Here are the important steps in building an effective RAG pipeline: Data Curation The foundation of any RAG pipeline begins with data preparation and curation. Using platforms like Encord which is designed for data centric approaches helps in streamlining this process. It helps in shifting from traditional LLMS to RAG pipeline but streamlining the process of transforming raw data into structures, ready-to-use knowledge bases. Curate the right data to power your AI models with Encord Index. Try it today and unify multimodal data from all local and cloud data sources to one platform for in-depth data management, visualization, search and granular curation. Document Processing Encord provides automated tools for parsing and structuring documents, regardless of the format. Whether working with PDFs, HTML files, or plain text, the platform ensures the data is uniformly processed. Content Chunking Once the documents are processed, it is divided into manageable chunks. These chunks are sized to optimize embedding generation and retrieval accuracy, balancing granularity with contextual preservation. Context-aware chunking ensures that essential relationships within the data are retained, improving downstream performance. Embedding Generation and Storage The next step is creating embeddings of the curated data. These embeddings serve as the basis for similarity search and retrieval in the RAG pipeline. These embeddings are stored in vector databases such as FAISS, Pinecone, or Weaviate. These tools ensure that the embeddings are indexed efficiently, enabling fast, scalable retrieval during real-time queries. Retrieval System Implementation The information retrieval system is the bridge between the user query and the external knowledge base, ensuring relevant information is delivered to the generative model. The system uses similarity search algorithms to match queries with stored embeddings. For cases needing both keyword precision and contextual understanding, hybrid retrieval approaches combine lexical and semantic search techniques. Context aware ranking systems then refine the retrieval process of the indexed data. By considering query intent, metadata, and feedback loops, these systems aim the most relevant results. This ensures the generative model receives high-quality inputs, even for complex or ambiguous queries. Common Pitfalls and How to Avoid Them While RAG pipelines are efficient, there are key challenges aswell that can affect their effectiveness. Identifying these pitfalls and implementing strategies to avoid them can help build reliable systems. Poor Data Quality Low-quality or poorly structured data can lead to irrelevant retrieval and reduce output accuracy. This includes outdated information, incomplete metadata, and unstructured documents. Solution Ensure proper data preprocessing, including automated structuring, cleaning, and add metadata. Use platforms like Encord to curate high-quality datasets. Inefficient Retrieval Systems A poorly implemented retrieval system may return irrelevant or redundant results, slowing down responses and affecting accuracy. Solution Research and try different retrieval techniques to find one apt for the project, such as hybrid retrieval approaches and optimized vector search algorithms, to improve relevance and reduce latency. Inconsistent Chunking Chunking content into inappropriate sizes can lead to loss of context or redundant retrieval, negatively impacting the generative stage. Solution Use chunking algorithms that preserve the context to balance granularity, ensuring each chunk captures meaningful data. Embedding Overhead Using generic embeddings or failing to optimize them for the domain can result in suboptimal retrieval accuracy. Solution Use domain-specific embeddings and train models on relevant datasets to improve retrieval precision. Scalability Bottlenecks As knowledge bases grow, retrieval systems may struggle with latency and indexing inefficiencies. Solution Adopt scalable vector databases and ensure periodic optimization of indexes to handle large-scale data effectively. Best Practices for RAG Pipeline Development Here are some key recommendations: Data Quality: Prioritize preprocessing and data curation. Make sure the data is annotated, and the data is curated from relevant and up-to-date sources. The databases should be updated constantly. Optimize Embeddings for Your Domain: Use embedding models tailored to the target domain. For instance, healthcare applications may require embeddings trained on medical literature to improve retrieval precision. Use Hybrid Retrieval Systems: Combine lexical search with semantic search to balance exact matches with contextual understanding. Hybrid retrieval ensures robust handling of diverse query types. Monitor and Improve Continuously: Establish feedback loops to monitor pipeline performance. Monitor the performance with evaluation metrics and use these insights to refine data quality, improve ranking systems, and adjust retrieval algorithms. Ensure Scalability: Design the RAG pipeline to handle increasing data volumes. Choose scalable storage and retrieval systems and regularly optimize indices for performance. Use Intelligent Chunking: Use algorithms that segment content effectively, preserving context while optimizing chunk size for retrieval and generation stages. Using Encord in RAG Applications Encord is a comprehensive data platform designed to simplify dataset management, data curation, annotation, and evaluation. It helps you handle complex data workflows effectively. How Encord Helps in Creating RAG Systems Data Curation for Retrieval Systems: Encord supports the creation of high-quality datasets required for accurate knowledge retrieval. Encord supports multimodal data and provides features to create automated annotation workflows to ensure consistent quality of the curated data. Annotation for Fine-Tuning and Generation: Encord Annotate allows teams to annotate datasets tailored for specific use cases, ensuring the generative model has contextually relevant inputs. It provides a visual metric to assess the quality of the annotated data. Feedback Loops: Encord enables continuous dataset refinement by incorporating user feedback into the pipeline. It provides features to continuously monitor the performance of the model and quality metrics to identify the failure modes and issues in the model. Try Encord for Free to simply you data management, curation, annotation and evaluation when building RAG pipelines. Conclusion Retrieval-Augmented Generation is a powerful framework for improving AI systems with real-time, contextually relevant data. By combining retrieval and generation, RAG pipelines enable AI models to overcome the limitations of static knowledge, making them better suited for dynamic, information-rich tasks. RAG systems have applications across diverse fields, including GenAI-powered chatbots for real-time customer support, personalized education platforms that improve user experience, legal research tools for efficient question-answering, and dynamic content generation systems. By understanding the building blocks, common pitfalls, and best practices for RAG pipelines, you can unlock the full potential of this approach, creating smarter, more reliable AI solutions tailored to your needs.