Problem
Scenario
You are reviewing a generative AI system that answers user questions and may refuse, answer directly, or route to a safer fallback. The team wants a clear evaluation approach that balances safety, factual accuracy, and usefulness, and they need a framework for deciding when the model should answer versus abstain.
Question
How would you ensure AI responses are safe, accurate, and helpful?
What this tests
- LLM evaluation design across safety, accuracy, and helpfulness
- Hallucination measurement on verifiable prompts
- Calibration of model confidence
- Threshold tuning for answer versus abstain decisions
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