Optimizing GEPA for production: A test-driven approach to prompt engineering | Decagon

This post details production adaptations of the GEPA framework for prompt engineering optimization. Key findings include: 1) an optimal sample size range of 20-100 examples for balancing performance and compute, challenging the 'more data is better' convention; 2) the critical necessity of using frontier LLMs (e.g., GPT-4.1, GPT-5.2, Claude Opus) as reflection models, as smaller models fail to perform meta-cognitive reasoning; and 3) the effectiveness of length constraints (specifically a 1500-character limit) as regularization to prevent prompt bloat and overfitting, achieving 4x compression with only 0.8% performance degradation. The authors advocate for a test-driven, software engineering approach to prompt optimization.