arxiv_cl 92% Match Research Paper LLM Developers,Prompt Engineers,AI Product Managers,Software Engineers 4 weeks ago

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

large-language-models › evaluation

📄 Abstract

Abstract: 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.

Key Contributions

This paper analyzes prompt underspecification in LLMs, showing its fragility and negative impact on application reliability (2x regression likelihood, >20% accuracy drops). It proposes requirements-aware prompt optimization mechanisms that improve performance by 4.8% and advocates for a systematic process for managing underspecification.

Business Value

Enables the development of more robust and reliable LLM-powered applications, reducing development costs and improving user experience. It helps businesses build dependable AI solutions.

Paper Metadata

Innovation Type

Methodology/Framework

Deployment Feasibility

High, as the proposed methods focus on prompt engineering and development processes, which can be integrated into existing workflows.

Limitations Addressed

Fragility and unreliability of LLM behavior due to underspecified prompts.,Limited effectiveness of standard prompt optimizers.,Difficulty in ensuring consistent instruction following.,Conflicting requirements in prompts.

Performance Gains

4.8% average performance improvement using requirements-aware prompt optimization.

Technical Tags

prompt underspecificationLLM instruction followingprompt optimizationrequirements discoverymodel instabilityaccuracy regressionLLM application developmentprompt engineeringperformance monitoringrequirements management

Research Topics

LLM InteractionPrompt EngineeringAI Application DevelopmentModel RobustnessHuman-AI Interaction

Methods & Architectures

Analysis of prompt underspecificationRequirements-aware prompt optimizationProactive requirements discoveryEvaluation and monitoring Large Language Models (LLMs)

Applications & Tasks

LLM Application Development Chatbot Development AI Assistants Prompt UnderspecificationFragile LLM BehaviorInconsistent Instruction FollowingDifficulty in Building Reliable LLM Apps Understand and manage prompt underspecificationImprove performance of LLM applicationsDevelop reliable LLM interactionsEnhance instruction following

Datasets & Benchmarks

Benchmarks

Performance improvement of 4.8% on average over baselines using requirements-aware prompt optimization.

AccuracyPerformance StabilityInstruction Following Rate

Related Fields

Software EngineeringHuman-Computer InteractionAI Development PracticesNatural Language Understanding

Keywords

Prompt EngineeringLLM UnderspecificationInstruction FollowingPrompt OptimizationLLM ApplicationsModel ReliabilityRequirements EngineeringAI DevelopmentPerformance MonitoringPrompt Fragility

Academic Context

#LLM Interaction#Prompt Engineering#AI Application Development#Model Robustness#Human-AI Interaction

Commercial Potential

Potential Products

Prompt optimization toolsLLM application development frameworksAI requirement management platforms

Target Industries

Technology (Software Development)SaaSAny industry building LLM applications

Use Case Examples

Developing a customer service chatbot that consistently understands user requests.Building an AI writing assistant that reliably follows complex formatting instructions.Creating internal tools that accurately process user queries despite variations in input.

Competitive Edge

Addresses a fundamental challenge in LLM application development by providing systematic methods to manage prompt underspecification, improving reliability over ad-hoc prompt engineering.

Market Opportunity

Large, as prompt engineering and LLM application development are rapidly growing fields.

Revenue Models

Consulting serviceslicensing of prompt optimization tools.

Resource Requirements

Compute Needs

Low, primarily for prompt optimization and evaluation.

Data Requirements

Requires diverse prompts and corresponding LLM outputs for evaluation.

Deployment Constraints

Requires careful prompt design and iterative refinement.,Effectiveness can vary across different LLMs.

Scalability

The methodologies are scalable to large numbers of prompts and LLM applications.

Production Readiness

Maturity Level

Research/Methodology

Time to Market

1-2 years (for tools/frameworks)

Patent Potential

Low, focused on process and methodology.

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