{"ok":true,"article":{"slug":"from-monolithic-to-modular-segment-level-automatic-prompt-optimization-25be1305","title":"From Monolithic to Modular: Segment-level Automatic Prompt Optimization","url":"https://arxiv.org/abs/2608.11219","canonical":"https://www.aimode.news/article/from-monolithic-to-modular-segment-level-automatic-prompt-optimization-25be1305","sourceName":"arXiv cs.AI","summary":"arXiv:2608.11219v1 Announce Type: new Abstract: 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 across SQuADv2, TweetEval, XSUM, CommonGen, and GSM8K on GPT-3.5-Turbo and GPT-4o-mini, SAPO achieves the best average score against Zero-shot and strong APO baselines including APE, OPRO, EvoPrompt, GEPA, and StraGO.","category":"AI","image":null,"lang":"en","publishedAt":"2026-08-13T04:00:00+00:00","createdAt":"2026-08-13T04:00:14.554378+00:00"}}