Automatically Evolving Prompt Guidelines for Task-Specific Optimization
The paper introduces AGOPS, an automatic method to generate task-specific prompt guidelines that help users write better prompts, improving LLM performance by recovering large performance drops from underspecification.
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[Submitted on 7 May 2026]
Title:Automatically Evolving Prompt Guidelines for Task-Specific Optimization
View a PDF of the paper titled Automatically Evolving Prompt Guidelines for Task-Specific Optimization, by Cedric Richter and 2 other authors
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Abstract:For Large Language Models to reliably answer user queries, users must clearly specify requirements, context, and constraints. In practice, however, user queries are often underspecified, forcing models to infer unstated assumptions that may misalign with the actual user intent. Existing prompt engineering guidelines aim to mitigate this issue, they are typically generic and task-agnostic, limiting their practical utility. Additionally, existing guidelines are formed manually and in a non-systematic way. To this end, we study prompt guideline optimization: the problem of automatically generating task-specific guidelines that help write better-specified prompts for a given task and model. Our key observation is that existing (completed) task examples (aka reference answers) often implicitly encode the missing information required to complete underspecified queries, including behavioral constraints, contextual assumptions, and evaluation criteria. We therefore propose AGOPS, an automatic approach that evolves task-specific guidelines via an optimization scheme that involves a prompt LLM writer, a solver LLM and prompt evolution, which maximize downstream effectiveness on a set of examples (user queries with reference answers). At inference time, our guidelines help users write well-specified prompts, boosting the effectiveness of LLMs. We show across mathematical reasoning, medical question answering, and coding tasks, that prompt underspecification leads to major drops (up to 95.3%) in downstream task performance (compared to well-specified prompts) and, perhaps more importantly, that this drop can hardly be recovered by existing prompt optimization techniques. Users following AGOPS guidelines can regain this loss (increasing performance between 15.5 to 81.7% on average) consistently across all benchmarks.
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.14105 [cs.CL]
(or arXiv:2607.14105v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.14105
arXiv-issued DOI via DataCite
Submission history
From: Cedric Richter [view email] [v1] Thu, 7 May 2026 15:50:42 UTC (584 KB)
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