What Prompts Don't Say: Understanding and Managing Underspecification in LLM Prompts

kaestner@cs.cmu.edu (C. Yang, Y. Shi, Q. Ma, M. Liu, C. Kästner, T. Wu)
Mon, 13 Jul 2026 11:18:49 GMT

Chenyang Yang, Yike Shi, Qianou Ma, Michael Xieyang Liu, Christian Kästner, and Tongshuang Wu. What Prompts Don't Say: Understanding and Managing Underspecification in LLM Prompts. In Proceedings of the Annual Meeting of the Association for Computational Linguistics -- Findings (ACL), pages 9072--9101, July 2026.

Prompt underspecification is a common challenge when interacting with LLMs. In this paper, we present an in-depth analysis of this problem, showing that while LLMs can often infer unspecified requirements by default (41.1%), such behavior is fragile: Under-specified prompts are 2x as likely to regress across model or prompt changes, sometimes with accuracy drops exceeding 20%. This instability makes it difficult to reliably build LLM applications. Moreover, simply specifying all requirements does not consistently help, as models have limited instruction-following ability and requirements can conflict. Standard prompt optimizers likewise provide little benefit. To address these issues, we propose requirements-aware prompt optimization mechanisms that improve performance by 4.8% on average over baselines. We further advocate for a systematic process of proactive requirements discovery, evaluation, and monitoring to better manage prompt underspecification in practice.