dspy-gepa
Instruction Optimization with dspy.GEPA
Guide the user through using dspy.GEPA to automatically discover better instructions for their DSPy programs through reflective evolution.
What is dspy.GEPA
dspy.GEPA is a DSPy optimizer that evolves the instruction text in your program's predictors. Rather than adding few-shot examples (like BootstrapFewShot) or tuning model weights (like BootstrapFinetune), GEPA iteratively proposes, evaluates, and refines the natural-language instructions that guide each LM call.
Key properties:
- Tunes instructions only -- no few-shot demos are injected into prompts, keeping them compact
- Uses textual feedback -- a reflection LM reads execution traces and failure feedback to propose better instructions, not just scalar scores
- Maintains a Pareto frontier -- tracks multiple candidate programs that excel on different subsets, then merges the best traits
- Works with ~50 examples -- needs less data than MIPROv2 or BootstrapFewShotWithRandomSearch
- Supports per-predictor feedback -- metrics can return targeted feedback for individual predictors in multi-step pipelines
When to use GEPA
Use dspy.GEPA when:
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