AI Engineering Answer Key

Evaluation principles

Strong answers separate model target from product decision, quarantine final evidence, use a credible non-AI baseline, report uncertainty and risk slices, and treat human or model judges as measurement instruments requiring validation.

Security principles

Retrieved text and user input are untrusted. The model never grants its own authority. Tool access is least-privileged and enforced outside the model with typed arguments, confirmation where needed, isolation, limits, and audit. Tests include indirect injection, exfiltration, poisoning, and legitimate unusual requests.

Operations principles

The deployed version includes model, tokenizer, prompt, retrieval/index, policies, code, and environment. Decisions use tail latency, successful-outcome cost, quality and safety gates, version-correlated telemetry, progressive delivery, tested rollback, fallback, and stop authority.

Oral-defense calibration

Pass: traces claims to artifacts, explains mechanisms, identifies uncertainty, and changes a decision when counterevidence warrants it. Repeat: relies on fluent description, cannot reproduce results, hides failures, or treats safety and fallback as prose.