What Is RAG and Why Use It?
Retrieval-Augmented Generation combines information retrieval with AI text generation. Instead of relying solely on a model's training data, RAG retrieves relevant documents from your knowledge base and includes them as context for the AI to reference when generating answers.
This dramatically reduces hallucinations and ensures responses are grounded in your actual, current data.
The RAG Architecture
Indexing: Split your documents into chunks, convert each chunk to a vector embedding, and store them in a vector database. Retrieval: When a user asks a question, embed the question and find the most similar document chunks.
Generation: Send the retrieved chunks along with the user's question to an LLM. The model generates an answer using the provided context, citing specific sources.
Implementation Tips
Chunking matters: Too small and you lose context. Too large and you dilute relevance. Experiment with 200-500 token chunks with overlap. Embedding models: Use models optimized for retrieval (not general-purpose models).
Reranking: After initial retrieval, use a reranker model to improve the ordering of results. Metadata filtering: Add metadata to chunks (date, source, category) to enable filtered search.
Common Pitfalls
Poor chunking strategy, using the wrong embedding model, not handling when no relevant documents are found, and ignoring the quality of source documents. RAG is only as good as the knowledge base it retrieves from.
For a practical example, see our guide on building a custom chatbot with RAG.