Understanding of RAG

21/08/2025 19:15

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GenAI

LLM

RAG

Vector Database

Retrieval-Augmented Generation (RAG) is a cutting-edge approach that combines the strengths of large language models (LLMs) with external knowledge sources, such as vector databases. By integrating retrieval mechanisms, RAG systems can access up-to-date and domain-specific information, significantly improving the relevance and accuracy of generated responses.

What is RAG?

RAG models work by retrieving relevant documents or data from an external source (like a database or search engine) and then using that information to generate more informed and context-aware outputs. This hybrid approach addresses the limitations of LLMs, such as outdated training data or hallucinations.

RAG Retrieval Techniques

RAG systems rely on efficient retrieval techniques to fetch relevant information. Common techniques include:

Dense Retrieval: Uses neural network encoders to convert queries and documents into dense vectors, enabling similarity search in vector space. This approach is robust for semantic matching.
Sparse Retrieval: Relies on traditional keyword-based methods (e.g., BM25, TF-IDF) to match queries with documents. Often used in combination with dense retrieval for improved accuracy.
Hybrid Retrieval: Combines dense and sparse retrieval methods to leverage the strengths of both, increasing recall and precision.
Nearest Neighbor Search: Utilizes algorithms (such as approximate nearest neighbor search) to quickly identify the most similar vectors in large datasets, ranking documents based on their proximity to the query vector. This is fundamental for efficient and scalable retrieval in vector databases.
Re-ranking: After initial retrieval, a secondary model (often a cross-encoder) re-ranks the top results for better relevance.

These techniques ensure that RAG systems can efficiently and accurately retrieve the most pertinent information for generation.

Vector Databases in RAG

Vector databases are specialized storage systems designed to efficiently manage and search high-dimensional vector representations of data. In RAG systems, these databases store embeddings of documents or knowledge chunks, enabling rapid similarity search and retrieval. This allows RAG models to fetch the most relevant information based on semantic similarity, which is crucial for generating accurate and context-aware outputs. The specific flow is like this: Documents are chunked -> chunks are embedded -> embeddings are stored in a vector database -> queries retrieve relevant chunks.

Query Parsing

Query parsing is a vital preprocessing technique in Retrieval Augmented Generation (RAG) that transforms a raw user query into an optimized format to dramatically improve retrieval accuracy. This is achieved through several key strategies: Query Decomposition breaks complex questions into simpler, parallel sub-queries; Intent Classification identifies the user’s goal to route the query to the correct tool or data source; Metadata Extraction parses out concrete filters like dates or categories to narrow the search; and advanced methods like Hypothetical Document Embeddings (HyDE) use a generated hypothetical answer to guide conceptual retrieval. By applying these refining techniques, the system ensures the most relevant context is retrieved, leading to more accurate, robust, and efficient AI-generated answers. This process is fundamental for evolving a basic RAG prototype into a production-ready system.

Embedding techniques

At the heart of any effective RAG system lies a critical choice: how to transform words into numbers that a machine can understand. This is the art of embedding, and the technique you select dramatically impacts the system’s performance. For fast, scalable retrieval from massive knowledge bases, bi-encoders are the workhorse; they process queries and documents independently, allowing for pre-computed indexes that deliver lightning-fast results. When sheer accuracy is paramount, cross-encoders perform a deeper, joint analysis of a query-document pair to produce a precise relevance score, making them perfect for reranking top candidates. For the best of both worlds, a hybrid approach leverages a bi-encoder for initial broad retrieval and a cross-encoder to finely polish the results. Beyond these fundamentals, advanced methods like ColBERT use token-level embeddings for nuanced understanding, while innovative strategies are emerging to dynamically adapt retrieval based on the LLM’s confidence, pushing the boundaries of accuracy and intelligence in AI-assisted search.

RAG in Multi-Agent Systems

In multi-agent AI systems, multiple agents collaborate to solve complex tasks. RAG can be leveraged by each agent to retrieve external knowledge independently or share retrieved information among agents. This enhances the collective intelligence of the system, allowing agents to access broader and more relevant knowledge, coordinate actions, and improve decision-making.

Example Use Case

Consider a product catalog management system where multiple AI agents collaborate to automate the extraction, validation, and ingestion of product attribute values from diverse sources such as supplier feeds, manufacturer websites, and user submissions.

When new product data arrives, an extraction agent initiates the process by leveraging RAG. It queries a vector database populated with historical catalog entries, attribute definitions, and documentation to retrieve relevant examples and standards for each attribute (e.g., color, size, material). Using this context, the agent accurately extracts candidate attribute values from the incoming data, even when formats or terminology vary across sources.

Next, a validation agent uses RAG to cross-reference the extracted values against trusted sources and catalog standards. By retrieving authoritative examples and rules from the vector database, the agent can detect inconsistencies, anomalies, or non-standard values, flagging them for review or correction.

Once validated, an ingestion agent coordinates with the other agents to map the approved attribute values to the correct catalog schema. It ensures that the new or updated product entries are correctly set up in the catalog database, handling any necessary transformations or enrichment steps.

This multi-agent, RAG-driven workflow streamlines the onboarding and setup of new products, reduces manual effort, and significantly improves the accuracy and consistency of catalog data. By continuously learning from historical entries and evolving standards, the system adapts to new product types and data sources, ensuring scalable and high-quality catalog management.

Considering using RAG

when to use RAG

When you need an AI to answer questions or perform tasks based on specific, proprietary, or private information that is not contained in its base training data.

RAG vs Finetuning

Think of a base LLM (like GPT-4) as a brilliant, general-purpose intern.

RAG is like giving this intern access to a specific, well-organized filing cabinet (your knowledge base) and teaching them how to quickly look up relevant information to answer a question. Their underlying skills don’t change, but their access to information does.

Fine-Tuning is like sending the intern to a training course to master a new skill (e.g., legal analysis) or internalize a specific style (e.g., writing all reports in your company’s format). You are changing their underlying skills and behavior.

RAG is primarily used to give the model access to new, external knowledge. It’s about the what. RAG is best for: “I need the model to know about this specific information.”

Fine-Tuning is primarily used to change the model’s inherent style, format, or task-specific behavior. It’s about the how. Fine-Tuning is best for: “I need the model to perform this task or write in this style.”