Retrieval-Augmented Generation (RAG)

What Is Retrieval-Augmented Generation (RAG)?

RAG solves the biggest problem with LLMs: they only know what they were trained on. A RAG system retrieves relevant documents from your knowledge base, then feeds them to the LLM as context — giving accurate, up-to-date answers grounded in your data.

RAG architecture: (1) Ingest documents, (2) Split into chunks, (3) Convert to embeddings, (4) Store in vector database, (5) At query time: search for relevant chunks, (6) Feed to LLM with the question, (7) LLM generates answer citing your sources.

RAG vs fine-tuning: Use RAG when your data changes frequently, you need source citations, or you have limited training data. Use fine-tuning when you need the model to learn your domain language or tone.