Self-optimizing prompt layer runs A/B tests and auto-promotes winners without redeployment

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A developer has built a prompt management system that stores AI prompts as versioned database rows instead of static code files, allowing live swaps without redeployment. Each agent fetches the active prompt at request time, and performance is scored using a weighted formula combining explicit user feedback and real business outcomes rather than self-grading by the model. A daily optimization cycle identifies underperforming prompts, rewrites them using an LLM, and opens a 90/10 A/B test to compare old and new versions. Once a rewritten prompt accumulates at least 50 samples and outscores the original by 10 points, it is automatically promoted as the new active version. The approach replaces guesswork-driven prompt editing and code deployments with a data-driven feedback loop tied to measurable results.

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