AI 协作标准

To prevent AI hallucinations, enforce evidence-based responses by mandating source attribution and certainty labeling, ensuring the AI grounds its output in provided code or documentation rather than fabricating information.

Certainty labeling in AI-generated content classifies statements as confirmed, inferred, assumed, unknown, or needing confirmation, ensuring users understand the reliability and factual basis of the AI's response.

To make an AI cite source files and line numbers, apply collaboration standards that mandate source attribution, forcing the AI to reference exact code locations and documentation links for every factual claim it generates.

You need evidence-based AI responses for software engineering tasks when analyzing project code or documentation, ensuring accuracy, avoiding speculative outputs, and maintaining strict reliability in technical explanations.

Yes, you can use AI collaboration standards with external documentation references, as the system supports generating factual content grounded in project code, internal documentation, and external links while applying certainty labels.

A limitation of using certainty labeling for AI response accuracy is that it relies entirely on the provided references, meaning the AI cannot verify facts outside its given context and will label such information as unknown or needing confirmation.