From Monolithic to Modular: Segment-level Automatic Prompt Optimization
Automatic Prompt Optimization (APO) often rewrites prompts monolithically, which can improve one behavior while degrading others. We present SAPO, a segment-level APO method that decomposes prompts into role, context, tasks, and output format, then applies targeted improvements based on top-5 and bottom-5 examples. The optimization loop uses one LLM with static meta-prompts and structured outputs for segmentation, weakness analysis, and candidate generation. We describe a train/validation protocol and a two-stage generation process: (1) segment-level diagnosis and recommendation extraction, (2) candidate synthesis constrained by weak/strong segment signals. Using the evaluation setup acro...
| Authors | Nikita Kulin, Viktor Zhuravlev, Artur Khairullin et al. |
|---|---|
| Primary category | cs.AI |
| Published | 21 July 2026 |
| Last updated at source | 21 July 2026 |
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