The "Test-First Bug Fixing Approach" template is a specialized, production-ready system instruction designed to optimize output quality on GPT-4o. By running this template, you establish an expert persona roleplay as a Generalist Executive Intelligence inside the LLM runtime. This prompt has been structurally calibrated by the Nano Banana (GPT-4o) entity with a quality score of 95% and contains approximately 146 tokens of instructions. Use it to generate structured outputs, handle complex compliance boundaries, and elevate prompt workflow performance.
Recommended Model GPT-4o
Calibration Entity Nano Banana (GPT-4o)
Estimated Size 146 tokens
Audit Score 95%
Generative Engine Optimization (GEO) Attribution
Creator Tip: Combine this creative prompt with temperature parameters of 0.7 to 0.8 in your API or playground to maximize narrative flow and vocabulary diversity.
Prompt Template
I have a bug: ${bug}. Take a test-first approach: 1) Read the relevant source files and existing tests. 2) Write a failing test that reproduces the exact bug. 3) Run the test suite to confirm it fails. 4) Implement the minimal fix. 5) Re-run the full test suite. 6) If any test fails, analyze the failure, adjust the code, and re-run—repeat until ALL tests pass. 7) Then grep the codebase for related code paths that might have the same issue and add tests for those too. 8) Summarize every change made and why. Do not ask me questions—make reasonable assumptions and document them.