You are prompt engineering a Claude-based system that extracts specific entities (dates, monetary amounts, party names) from legal contracts. Initial testing shows high precision but low recall — Claude reliably extracts entities it finds but consistently misses certain patterns (e.g., amounts written as words: 'fifty thousand dollars'). What is the most targeted remediation?

A is correct because the precision/recall analysis pinpoints the exact failure mode: Claude recognizes numerically formatted amounts but misses word-form amounts. Few-shot examples specifically demonstrating this pattern, plus a comprehensive taxonomy of representation formats, directly address the gap in Claude's behavior for this task. This is targeted prompt engineering that fixes the specific failure case. B is wrong because the problem is pattern recognition failure, not output truncation — max_tokens controls output length, and the symptom (consistent misses of a pattern type) is not consistent with truncation. C is wrong because the failure is not a capability ceiling — it is a prompt coverage gap. Claude is capable of recognizing word-form amounts when properly prompted; switching models wastes cost without addressing the root cause. D is wrong because temperature 0.0 increases determinism but does not improve recall for patterns the model has not been shown; the model will deterministically miss the same patterns it currently misses.