agentsop-prompt-compilation

What problem does it solve?

It prevents wasting expensive prompt-optimization runs by ensuring you only compile an optimizer when you have a validated metric and enough labeled data for the optimizer you intend to run.

Core Features & Use Cases

• Two-gate readiness decision: checks that the metric exists and is human-validated (at least 20 spot-checks) and that the labeled example count clears the floor for the chosen optimizer.
• Optimizer selection by constraints: picks an optimizer based on data scale and whether you have textual feedback (e.g., GEPA can work with far fewer examples when feedback is rich).
• Cost discipline and escalation rules: starts with a cheap probe (auto="light"), escalates only on meaningful lift, and confirms gains on a held-out test set rather than the optimization validation set.

Quick Start

Ask your coding/agent workflow to decide whether it is compile-ready for DSPy prompt auto-optimization, returning a go/no-go gate and the recommended optimizer choice.