Iterative Resolution of Prompt Ambiguities Using a Progressive Cutting-Search Approach

TL;DR

This paper introduces an iterative, structured method for resolving natural language prompt ambiguities in generative AI, improving accuracy and user satisfaction over traditional one-shot approaches.

Contribution

It presents a novel progressive cutting-search approach that systematically clarifies ambiguities through questions and examples, enhancing AI response precision.

Findings

Higher accuracy in resolving ambiguities

Faster resolution times compared to baseline methods

Increased user satisfaction with iterative clarification

Abstract

Generative AI systems have revolutionized human interaction by enabling natural language-based coding and problem solving. However, the inherent ambiguity of natural language often leads to imprecise instructions, forcing users to iteratively test, correct, and resubmit their prompts. We propose an iterative approach that systematically narrows down these ambiguities through a structured series of clarification questions and alternative solution proposals, illustrated with input/output examples as well. Once every uncertainty is resolved, a final, precise solution is generated. Evaluated on a diverse dataset spanning coding, data analysis, and creative writing, our method demonstrates superior accuracy, competitive resolution times, and higher user satisfaction compared to conventional one-shot solutions, which typically require multiple manual iterations to achieve a correct output.