What is Retrieval-Augmented Generation (RAG), and when should you use it?
Retrieval-Augmented Generation (RAG) retrieves relevant material from an external knowledge source and supplies it to a language model alongside the user’s question. A typical system chunks and indexes documents, retrieves likely matches at query time, and asks the model to answer using that context. RAG is useful when answers should reflect private, changing, or cited information, but retrieval quality and source quality still limit the result.
Prepare documents (ingestion)
01DocumentsApproved sources and metadata
02Chunk + embedMake passages searchable
03IndexStore text, vectors, and access scope
Answer a question (runtime)
01QuestionUser asks for information
02RetrieveFind and rank allowed passages
03GenerateAnswer from supplied evidence
The first two nodes run during ingestion; retrieval and generation happen for each query. A production design also needs permissions, freshness, and evaluation.