Evaluating Generative AI Use and Accuracy

# Evaluating Generative AI Use and Accuracy Discuss how you decide whether generative AI belongs in a software product or workflow. Ground the answer in a project you understand, then cover appropriate and inappropriate use cases, how you define accuracy, and how you manage errors after launch. ### Constraints & Assumptions - The system may produce fluent but incorrect output. - Accuracy must be defined against the user task, not only a generic model benchmark. - Sensitive data, latency, cost, and human review may affect the design. ### Clarifying Questions to Ask - What consequence follows from a wrong answer, and can a person review it before action? - Is the expected output factual, creative, classificatory, or a tool call? ```hint Begin with failure cost The same model quality may be acceptable for drafting and unacceptable for an irreversible decision. ``` ### What a Strong Answer Covers - A clear task boundary and comparison with simpler approaches. - Task-specific offline evaluation and representative test sets. - Guardrails, retrieval or tool grounding where appropriate, and abstention. - Production monitoring, feedback, versioning, and rollback. ### Follow-up Questions - How would you evaluate a task with several acceptable answers? - What would make you remove the model from the critical path?