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.
How would you ensure AI responses are safe, accurate, and helpful?
LLM evaluation design across safety, accuracy, and helpfulnessHallucination measurement on verifiable promptsCalibration of model confidenceThreshold tuning for answer versus abstain decisions