A deep dive into DSPy’s GEPA reveals that its per‑example Pareto frontiers, weighted parent selection, and mini‑batch reflection loop are the real drivers of performance – not just the surface API. By dissecting the source and experimenting with valset composition, merge settings, and proposer prompts, the author uncovers practical guidelines that can turn an underwhelming run into a powerful, exploration‑driven optimization.