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The core retrieval tradeoff: recall is 'did I get all the relevant chunks,' precision is 'are the chunks I got actually relevant.'
Crank up how many results you return and recall rises (you miss less) but precision drops (more junk dilutes the context); tighten it and the reverse. For RAG both hurt you differently — low recall means the answer simply isn't in context, low precision means the model wastes attention on noise and may ground on the wrong passage. The usual move is high recall on retrieval then a reranking pass to restore precision before the top few hit the prompt. Example: retrieve 50 chunks (recall), rerank, keep the best 5 (precision).
Plainly
Think of Recall vs Precision as a simple recipe for doing the work better. The core retrieval tradeoff: recall is 'did I get all the relevant chunks,' precision is 'are the chunks I got actually relevant.'
In practice
Use it when you need a repeatable method instead of guessing from vibes. In practice, define the owner, input, output, and failure mode before you rely on it.