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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Sep 4·5 min read
ESPO: Error-Structured Prompt Optimization via Diagnose, Diversify, and Stabilize
TL;DR
ESPO (Error-Structured Prompt Optimization) enhances prompt optimization by addressing issues like prompt bloat, achieving a 3.76 percentage point accuracy improvement over GEPA while producing prompts 47% shorter.
✦ Why It Matters
Implement ESPO to optimize your prompt generation process and improve model accuracy today.
Key Takeaways
- Adopt ESPO to reduce prompt length and improve accuracy.
- Utilize the Diagnose phase to identify error patterns effectively.
- Incorporate diverse strategies in the Propose phase for better candidates.
- Apply bootstrap stability selection to enhance reliability in prompt selection.
How It Works
ESPO operates in three phases: Diagnose clusters training errors into structural patterns, Propose generates diverse prompt candidates using four independent strategies, and Select applies bootstrap stability selection to ensure that only the most reliable prompts are chosen. This structured approach addresses the deficiencies of previous methods, leading to more effective and concise prompts.