Retrieval-augmented generation (RAG)

Retrieval-augmented generation (RAG) is a technique where an AI model fetches relevant information from a knowledge source at query time and uses it to ground its answer.

RAG reduces hallucination by giving the model real, current context instead of relying only on what it memorised during training. The quality of a RAG system depends heavily on the quality and governance of the knowledge it retrieves from.

Building production-grade RAG, with ingestion, indexing, permissions, and source-tracing, is a significant engineering effort.

MindKeepr provides governed, permission-aware retrieval as a managed layer, so teams get the benefit of RAG without building and maintaining the pipeline themselves.