Assessing Where Generative AI Fits and How Accurate It Is

Explain the way you determine if generative AI should be part of a software product or workflow. Anchor your response in a project you know well, then address suitable and unsuitable applications, what accuracy means for the task, and how you handle mistakes once the system is live.

Constraints and Working Assumptions

• The model can generate text that reads well yet is wrong.
• Accuracy has to be measured against the actual user task, not just a standard model benchmark.
• Design choices may also be shaped by sensitive data, latency, cost, and the need for human review.

Questions to Clarify First

• If the answer is wrong, what happens as a result, and is there a chance for a person to check it before anyone acts?
• Is the output supposed to be factual, creative, a classification, or a call to a tool?

Hint — Start with failure cost: A model of the same quality can be fine for drafting but not for a decision that cannot be reversed.

Elements of a Strong Answer

• A well-defined boundary for the task and a comparison against simpler methods.
• Offline evaluation tailored to the task and test sets that represent real usage.
• Guardrails, grounding through retrieval or tools when useful, and the ability to abstain.
• Monitoring in production, feedback loops, versioning, and rollback capability.

Follow-up Questions

• How would you assess a task where more than one answer can be acceptable?
• What conditions would lead you to take the model off the critical path?

Overview: Talk about past generative-AI work, when generative AI is the right tool, patterns that work well in practice, and how to measure and raise output accuracy.