Prompt Engineering & Optimization
GEPA-DSPy Prompt Optimization for Profile Matching
Applied reflective/evolutionary prompt optimization (GEPA via DSPy) to a candidate profile dedup and merge-decision system, cutting prompt development time from weeks to hours.
DSPyGEPAOptunaOpenRouterUnslothArgilla
Given two candidate profiles, decide field-by-field whether they refer to the same person and whether they should be merged. Instead of hand-tuning prompts, used dspy.GEPA to reflectively evolve per-field and combined-signature prompts against a custom weighted metric, then distilled the optimized prompt’s behavior into a fine-tuned Qwen3 model via Unsloth for cheap production inference.
Key Results
- →10–15% accuracy lift over the legacy hand-written prompt baseline
- →Cut prompt development cycle from days/weeks down to hours
- →Per-field + combined DSPy signatures with custom score/feedback metrics
- →Merge-decision F1 of 0.943 on the resulting fine-tuned model