用 GEPA 优化 Jev 提示词做药物不良反应句识别
作者把 Jev 接到提示词优化器 GEPA 上，在 300 条医学文献句子上把 F1 从 69.1% 提到 79.7%，误报减半。
TypeSafe AI's Jev kept coming up, so I decided to test it on a task using public medical literature. Jev lets you describe a decision in natural language and get structured answers with probabilities. I used it to identify sentences reporting suspected adverse drug effects. It found most relevant sentences, but also flagged many negatives and sometimes gave incorrect answers complete confidence. I then connected Jev to GEPA, a prompt optimizer, to see whether clearer instructions would help. An assistant proposed revisions based on training errors, and GEPA evaluated the candidates. The selected prompt clarified the evidence needed in each sentence: a drug, a harmful effect, and a relationship between them. On the same 300 fresh test sentences: ✅ F1 increased from 69.1% to 79.7%. ✅ False positives fell from 47 to 22. ✅ Probability error, measured by Brier score, fell by about 45%. 🔗https://praneeth16.github.io/blog/adapting-jev-with-gepa