Large language models are exceptionally good at producing fluent text. They are not, by default, good at staying current, respecting boundaries of private knowledge, or documenting the sources of an answer. That gap is exactly where most AI products fail: the demo looks impressive, but the system is not trustworthy when users rely on it.
Retrieval-augmented generation (RAG) closes the gap by designing an evidence path. Instead of letting the model “reason from memory,” you route the request through retrieval, enforce access rules, collect supporting sources, and then ask the model to answer from those sources with citations. In practice, RAG is less about prompting and more about engineering: a data pipeline, a contract, and an operational loop.