Closed-Loop Prompt Optimization Framework for Production AI Systems
blaizedsouza · x · 2026-08-15
Introduces a Closed-Loop Prompt Optimization Framework for production systems to address slow manual iteration. The workflow includes: 1. Collecting quality signals from production (thumbs, corrections, downstream success); 2. Identifying underperforming variants; 3. Generating and testing improved candidates offline; 4. Shadow testing promising prompts; 5. Promoting winners and retiring losers; 6. Keeping humans in the loop for safety-critical changes. The core principle is to enable continuous improvement based on real usage.
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