Question: How do teams handle AI code hallucinations in practice?
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- Dominant language
- Kotlin
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Description
Hey @phodal -- I came across AutoDev while researching how teams deal with AI code hallucinations (phantom imports, non-existent API calls, etc). Your work on the architecture-aware context in AutoDev is the closest thing I've seen to actually solving this at the generation stage rather than after the fact.
Quick question: in your experience, do most hallucination problems come from insufficient context (the model doesn't know the codebase), or is it more fundamental than that? And have you found static analysis catches enough of it, or do teams need something runtime-level?
Would appreciate your take -- no need for a long response, even a one-liner helps.
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Research direction
This issue asks for the maintainer's perspective on AI code hallucinations rather than requesting a repository change. No files, tests, or entry points are identified, and there is no implementation-defined completion condition; clarify whether a concrete feature, documentation update, or experiment is wanted before starting work.
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Assessment
- Domain
- ai, devtools
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 10/100