Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) is a technique that combines the power of large language models with external knowledge retrieval. Instead of relying solely on what the AI learned during training, RAG fetches relevant information from a knowledge base before generating a response.

Think of it like this: rather than asking someone to answer from memory, you're giving them access to a library of relevant documents first.

The process works in three steps:

1. Query: The user asks a question
2. Retrieve: The system searches a knowledge base for relevant information
3. Generate: The AI uses the retrieved context to formulate an accurate response

RAG solves several critical problems with traditional AI systems:

Reduces hallucinations: By grounding responses in actual documents, RAG significantly decreases the chance of the AI making things up. The model generates answers based on retrieved facts, not just statistical patterns.

Keeps information current: LLMs have a knowledge cutoff date. RAG allows you to provide up-to-date information without retraining the entire model.

Enables domain expertise: You can make a general-purpose AI an expert in your specific domain by connecting it to your proprietary documents, manuals, or databases.

Provides transparency: Users can see which sources informed the AI's response, building trust and enabling verification.

A RAG system consists of several interconnected components:

1. Document Processing First, your documents are broken into smaller chunks (typically 200-500 tokens). Each chunk is converted into a numerical representation called an embedding using a model like OpenAI's text-embedding-ada-002.

2. Vector Storage These embeddings are stored in a vector database (like Pinecone, Weaviate, or pgvector). Vector databases are optimized for similarity search—finding chunks that are semantically similar to a query.

3. Retrieval When a user asks a question, that question is also converted to an embedding. The system then searches the vector database for the most similar document chunks.

4. Context Injection The retrieved chunks are inserted into the prompt sent to the LLM, providing relevant context for generating the response.

5. Generation The LLM generates a response based on both the user's question and the retrieved context.

Both RAG and fine-tuning can customize AI behavior, but they serve different purposes:

Use RAG when: You need accurate recall of specific facts, documents change frequently, or you want source citations.

Use fine-tuning when: You need to change how the model writes, reasons, or handles specialized tasks.

Many production systems use both: fine-tuning for behavior and RAG for knowledge.

Building a RAG system involves several technical decisions:

Choose your embedding model Popular options include OpenAI's text-embedding-3-small, Cohere's embed-v3, or open-source models like BGE or E5. Consider cost, performance, and whether you need multilingual support.

Select a vector database

• Pinecone: Fully managed, easy to scale
• Weaviate: Open-source, feature-rich
• pgvector: PostgreSQL extension, great if you're already using Postgres
• Chroma: Lightweight, good for prototyping

Optimize chunk size Smaller chunks (200-300 tokens) provide more precise retrieval but may lack context. Larger chunks (500-1000 tokens) preserve more context but may include irrelevant information. Test different sizes for your use case.

Implement hybrid search Combine vector similarity search with keyword search (BM25) for better results. This catches both semantic matches and exact keyword matches.

Add reranking Use a cross-encoder model to rerank retrieved results before passing them to the LLM. This improves relevance significantly.

Challenge: Retrieved context is irrelevant Solution: Improve chunking strategy, add metadata filtering, implement reranking, or use query expansion to generate multiple search queries.

Challenge: Context window limits Solution: Summarize retrieved chunks, use a model with a larger context window, or implement iterative retrieval that fetches more context as needed.

Challenge: Inconsistent response quality Solution: Add evaluation metrics, implement A/B testing, use structured prompts that clearly separate context from instructions.

Challenge: Latency Solution: Cache common queries, use approximate nearest neighbor search, pre-compute embeddings for frequent queries, or implement streaming responses.