What Prompts Don’t Say: Understanding and Managing Underspecification in LLM Prompts
Chenyang Yang, Yike Shi, Qin Ma, Michael Xieyang Liu, Christian Kästner, Tongshuang Wu · 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: Underspecified prompts are 2x as likely to regress across model or prompt changes, sometimes with accuracy drops exceeding 20%. 1 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.* Now at Google DeepMind.