Precision vs recall trade-offs in extraction

Your team uses Claude to extract entities from medical referral letters. After deploying a stricter prompt, precision rose from 86% to 94% but recall dropped from 91% to 78%. The medical reviewers say missing entities is worse than spurious ones because doctors are trained to ignore extra detail but rarely catch omissions. What is the most appropriate next step?

1. A.Ship the stricter prompt and start a project to backfill the missing entities with a manual review queue.
2. B.Revert to the more lenient prompt and accept the lower precision — recall is the load-bearing metric for this use case.
3. C.Run both prompts in parallel and average their outputs.
4. D.Train a downstream model to fill in entities the stricter prompt missed.