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arXiv — NLP / Computation & Language · · 3 min read

From Monolithic to Modular: Segment-level Automatic Prompt Optimization

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Computer Science > Artificial Intelligence

arXiv:2608.11219 (cs)
[Submitted on 21 Jul 2026]

Title:From Monolithic to Modular: Segment-level Automatic Prompt Optimization

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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.
Comments: Accepted at the IJCAI-ECAI 2026 Workshop on Robustifying Generative AI for Reliable, Safe, and Human-Centric Systems (RobustifAI)
Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2608.11219 [cs.AI]
  (or arXiv:2608.11219v1 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2608.11219
arXiv-issued DOI via DataCite

Submission history

From: Artur Khairullin [view email]
[v1] Tue, 21 Jul 2026 16:02:04 UTC (3,485 KB)
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