RAG Systems Explained Simply (For Engineers Shipping to Prod)

RAG — retrieval-augmented generation — is the single most reliable way to make an LLM answer from your data instead of guessing. As an LLM Engineer, it is in almost every enterprise system I build.

You split your documents into chunks, turn each chunk into an embedding vector, and store them in a vector database. At query time you embed the question, retrieve the most similar chunks, and hand them to the LLM as context. The model answers from real text, not memory.

Chunk size and overlap decide whether retrieval finds the right passage. Your embedding model decides recall quality. Your vector database (I use Supabase pgvector for most projects) decides scale and cost. Get these three right and RAG feels like magic; get them wrong and it feels broken.

If your knowledge fits in a single prompt, skip the vector database. RAG earns its complexity only when the corpus is large or changes often. Engineering is knowing when not to add a moving part.