Introduction to RAG

Build a retrieval-augmented generation workflow that finds usable evidence and passes it to a language model. Examine parsing, chunking, compatible embeddings, retrieval ranking, and separate tests for retrieval quality and answer support.

Retrieval-augmented generation (RAG) supplies retrieved material as context for generation. A system like the earlier text service can use this context to answer questions about documents outside the model’s training data. The application performs retrieval; the model does not automatically gain unrestricted or current access to every source.

Retrieval can provide newer or more specific evidence, but freshness and accuracy depend on what the application indexes, retrieves, and passes to the model. If a current manual is missing from the index, generation cannot reliably recover it by being instructed to use current information.