Dev Tools · 1h ago
Self-Optimizing Prompt Layer A/B Tests and Auto-Promotes Winners
A new system stores prompts as versioned database rows, scores them on real business outcomes (not LLM self-evaluation), and runs daily A/B tests. Underperformers are rewritten by an LLM, tested at 90/10 traffic split, and winners auto-promoted after 50 samples with a 10-point lead. The approach replaces static prompt files with data-driven iteration, using a combined score of explicit feedback (40%) and implicit results (60%).
Meridian48 take
The key insight—scoring prompts on actual outcomes rather than LLM self-evaluation—avoids the circular reasoning that plagues most prompt optimization tools.
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Una capa de prompts que se califica a sí misma por resultados, hace A/B testing de sus propias reescrituras, e intercambia al ganador casi sin despliegue →
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prompt-engineeringab-testing