Enhancing LLM responses with external knowledge through document retrieval
Retrieval-Augmented Generation (RAG) combines the power of large language models with external knowledge sources. Instead of relying solely on what the model learned during training, RAG retrieves relevant information from your documents, databases, or other sources and includes it in the prompt—dramatically improving accuracy and reducing hallucinations.
RAG is a technique that enhances LLM responses by retrieving relevant context from external sources before generating an answer. Instead of asking the LLM to answer from memory alone, you:
This approach bridges the gap between the LLM's general knowledge and your specific, private, or up-to-date information.
LLMs have a knowledge cutoff date. RAG lets you include current information, recent documents, or real-time data.
Access your internal documents, company policies, or proprietary information that wasn't in the LLM's training data.
By grounding responses in retrieved documents, you reduce the chances of the LLM making up information.
You can cite which documents or sections the answer came from, making responses more trustworthy and auditable.
Break your documents into smaller chunks (typically 200-1000 tokens each). Convert each chunk into an embedding vector using a model like OpenAI's text-embedding-ada-002 or open-source alternatives. Store these embeddings in a vector database like Pinecone, Weaviate, Chroma, or FAISS.
When a user asks a question, convert their question into an embedding using the same model. Search the vector database for the most similar document chunks (using cosine similarity or other distance metrics).
Take the top N most relevant chunks and insert them into your prompt as context. This gives the LLM the information it needs to answer accurately.
The LLM reads the context and generates a response grounded in the retrieved documents. The response is more accurate because it's based on specific information rather than general knowledge.
Use this template structure when implementing RAG:
You are a helpful assistant that answers questions based on provided context. CONTEXT: {retrieved_document_chunk_1} {retrieved_document_chunk_2} {retrieved_document_chunk_3} INSTRUCTIONS: - Answer the question using ONLY information from the context above - If the context doesn't contain enough information, say so - Cite which section of the context you used (e.g., "According to Document 2...") - Do not make up or infer information not present in the context QUESTION: {user_question} ANSWER:
Too small (< 100 tokens): You lose context and may need many chunks. Too large (> 1000 tokens): Less precise retrieval and wastes context window. Sweet spot is typically 300-600 tokens per chunk with 10-20% overlap between chunks.
Combine semantic search (embeddings) with keyword search (BM25) for better results. Semantic search finds conceptually similar content while keyword search catches exact matches. A weighted combination often works best.
Store metadata with your chunks (document source, date, category, author). Filter by metadata before semantic search to narrow down the search space and improve relevance.
Retrieve more chunks than you need (e.g., top 20), then use a more sophisticated re-ranker model to select the truly most relevant 3-5 chunks. This improves precision significantly.
Explicitly instruct the LLM to say "I don't have enough information to answer this" when the retrieved context is insufficient. This prevents hallucinations.
Track which chunks are retrieved and whether they're relevant. Log user feedback. Adjust chunking strategy, retrieval parameters, and prompts based on real usage patterns.
Comprehensive framework with document loaders, text splitters, vector stores, and retrieval chains. Great for rapid prototyping.
Learn More →Specialized data framework for LLM applications with advanced indexing and retrieval strategies. Excellent for complex data structures.
Learn More →Managed vector database optimized for production RAG deployments. Handles billions of vectors with low latency.
Learn More →Open-source vector databases you can self-host. Great for privacy-sensitive applications or when you need full control.
Learn More →RAG is ideal when:
Consider alternatives when: You need the LLM to reason abstractly without specific context, you're doing creative generation, or your use case needs sub-100ms response times (retrieval adds latency).