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

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. 1 Code and data shared in https://github.com/ malusamayo/underspec-analysis .