Iterative Resolution of Prompt Ambiguities Using a Progressive Cutting-Search Approach
Journal:
arXiv
Published Date:
May 5, 2025
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.
