All terms
Glossary
RAG (Retrieval-Augmented Generation)
Grounding an LLM's answers in your own data retrieved at query time.
Retrieval-augmented generation (RAG) is a technique that grounds a large language model's responses in your own documents or data: relevant content is retrieved at query time and given to the model so answers are accurate and current instead of guessed.
RAG is the backbone of most enterprise AI assistants, copilots, and internal search tools because it keeps answers tied to source data and reduces hallucination.
Building a reliable RAG system involves ingestion, chunking, embeddings, a vector store, retrieval quality, and evaluation - all scopable work.
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