Progressive Disambiguation and Sycophancy Mitigation for Prompt Uncertainty in Generative AI

Generative AI systems increasingly support users in coding, data analysis, and creative tasks through natural-language interaction. However, user prompts are often underspecified or ambiguous, and current LLM-based assistants typically proceed with a plausible interpretation, leaving misalignments to be discovered only after the output is inspected or executed. This behavior can trigger costly trial-and-error prompt revisions and may be amplified by sycophantic tendencies that reinforce incorrect assumptions. We present a progressive disambiguation framework that resolves prompt uncertainty before producing a final answer. The framework separates intent clarification from solution synthesis by guiding users through a structured pre-generation dialogue that elicits missing constraints, compares alternative interpretations, and illustrates consequences via targeted input-output examples and representative edge cases. In addition, it performs incremental constraint consistency checks that flag implausible or conflicting user-provided assumptions and request explicit confirmation before generation. After incompatible interpretations are pruned and constraints are validated, the system generates a single, intent-aligned solution. Experiments on a diverse benchmark covering coding, data analysis, and creative writing show that our approach improves output accuracy, reduces corrective iterations and overall user effort, and achieves competitive end-to-end resolution time and higher user satisfaction compared to one-shot and reactive clarification baselines.