Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation combines the generative capabilities of large language models with the precision of information retrieval systems. Instead of relying solely on what the model learned during training, RAG first searches a knowledge base for relevant documents, then includes those documents as context for the model to reference when generating its response. This grounds the output in actual data rather than the model's potentially outdated or incorrect parametric knowledge.

A typical RAG pipeline works in three stages. First, your knowledge base documents are chunked into smaller segments and converted into vector embeddings stored in a vector database. Second, when a user asks a question, the query is also converted to an embedding and used to find the most semantically similar document chunks. Third, the retrieved chunks are inserted into the prompt along with the user's question, and the LLM generates an answer based on this augmented context.

RAG has become the go-to approach for building AI applications that need to reference company-specific data, recent information, or domain expertise that the base model does not have. It is cheaper and more flexible than fine-tuning because you can update the knowledge base without retraining the model. RAG also reduces hallucinations because the model can cite specific sources, and you can verify its claims against the retrieved documents.