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

What this post added

This post details the application of the GEPA prompt engineering optimization framework to a production classification task. It presents findings from 19+ ablation experiments, highlighting three critical adaptations for production readiness: 1) an optimal sample size range of 20-100 examples, contrary to conventional wisdom; 2) the non-negotiable requirement for frontier LLMs as reflection models, as smaller models fail to perform meta-cognitive reasoning; and 3) the essential role of length constraints (e.g., 1,500 characters) as regularization to prevent prompt bloat and overfitting, achieving 4x compression with minimal performance loss. The work advocates for treating prompt optimization as software engineering.