Retrieval-augmented generation
A pattern that retrieves passages relevant to a query and inserts them into the prompt before generation, rather than relying only on what the model learned during training. It depends on chunking to produce retrievable units and a vector database to find them quickly, and it fails quietly when either step is weak: a good model given a bad passage still answers from the bad passage.
Why exams ask this
Tested as "why not just fine-tune instead." The correct framing is a cost and freshness tradeoff: RAG updates by editing the index, fine-tuning updates by retraining. An exam asking you to fix a stale-knowledge problem is almost always pointing at RAG, not a new model.
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