RAG (Retrieval-Augmented Generation)

RAG (Retrieval-Augmented Generation)

RAG (Retrieval-Augmented Generation) — An AI architecture that retrieves relevant documents from a knowledge store and provides them to the model as context before generating an answer.

RAG is the standard way to ground LLM outputs in enterprise knowledge — policies, contracts, prior decisions — rather than relying on the model's training data alone.

How xyner approaches rag (retrieval-augmented generation)

xyner treats rag (retrieval-augmented generation) as a first-class platform concern — the relevant capability is documented and tested, with clear integration points for enterprise architecture teams.

For a deeper technical reference, see the related capability page or the corresponding whitepaper linked below.

See also

Related: vector database, semantic search, policy-aware RAG, grounding.