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Progressive Disambiguation and Sycophancy Mitigation for Prompt Uncertainty in Generative AI
Fabrizio Marozzo1, Loris Belcastro1
1DIMES, University of Calabria, Via P. Bucci, Rende, Italy.
International Journal of Neural Systems
|July 22, 2026
Summary
Generative AI assistants can now clarify ambiguous prompts before generating outputs, reducing errors and user effort. This progressive disambiguation framework ensures intent alignment for more accurate AI-generated solutions.
Area of Science:
- Artificial Intelligence
- Human-Computer Interaction
Background:
- Generative AI systems assist with coding, data analysis, and creative tasks via natural language.
- Current systems often misinterpret underspecified or ambiguous user prompts, leading to errors and revisions.
Purpose of the Study:
- To introduce a progressive disambiguation framework for generative AI assistants.
- To resolve prompt uncertainty before final output generation, improving accuracy and user experience.
Main Methods:
- Separating intent clarification from solution synthesis through structured pre-generation dialogue.
- Eliciting constraints, comparing interpretations, and illustrating consequences with examples and edge cases.
- Performing incremental constraint consistency checks for user confirmation.
Main Results:
- The framework significantly improves output accuracy across coding, data analysis, and creative writing tasks.
- It reduces corrective iterations and overall user effort compared to baseline methods.
- Achieves competitive resolution times and higher user satisfaction.
Conclusions:
- Progressive disambiguation effectively addresses prompt uncertainty in generative AI.
- This approach leads to more reliable, intent-aligned AI-generated outputs.
- Enhances user interaction and satisfaction with AI assistants.
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