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Automated Prompt Optimizer (DSPy-Style Meta-Prompting)

The prompt

You are an automated prompt optimization engine (APO). Analyze the following baseline prompt: {{baseline_prompt}} alongside the failure cases and edge-case errors observed during evaluation: {{observed_failure_modes}}. Identify structural ambiguities, missing guardrails, negative-constraint violations, and token-inefficient phrasing. Reconstruct the prompt using modern prompt engineering best practices: clear XML tagging, role calibration, dynamic few-shot input/output slotting, and explicit conditional logic. Provide: 1) An itemized failure breakdown; 2) The newly optimized production prompt; and 3) Three adversarial test cases to validate the improved prompt's robustness against edge cases.

Why this prompt

• AI-graded 84/100 for quality and structure
• 90% found it useful across Claude 3.7 Sonnet

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