Questions for clarifying LLM code generation assumptions
Questions to ask LLMs to ensure generated code matches intended behavior.
When using LLMs for code generation, especially for complex or underspecified tasks, it is crucial to clarify potential ambiguities.
This question bank helps developers probe LLMs to uncover hidden assumptions and ensure the generated code aligns with intended behavior. It focuses on identifying underspecified requirements and potential edge cases that could lead to errors.
Asking targeted questions about assumptions and alternative behaviors helps expose discrepancies between the LLM's interpretation and the user's intent.
When the requirements are fully specified and unambiguous, or when the LLM has a proven track record of perfect interpretation for the given task.
- CONTRA: Discovering and Qualifying Behavior-Changing Questions for Selective Clarification in LLM Code Generation ↗arXiv · arXiv cs.AI · Oct 1, 2026 · paraphrased
First seen on Subvolts Oct 1, 2026. Updated Oct 5, 2026. Spotted a problem? Tell us.
- CONTRA: Discovering and Qualifying Behavior-Changing Questions for Selective Clarification in LLM Code GenerationAnthropic · arXiv cs.AI · Oct 1, 2026
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Questions people ask
- Which AI tools does this work with?
- Any current model. It was seen with in the sources.
- Where did this come from?
- arXiv (arXiv cs.AI). Subvolts collected, normalized and credited it; the link above opens the original.