Defend

LLMOps and Responsible AI controls

A long list of controls invites “show me how that one works.” Pick the three you can explain most deeply.

On your resume

Define LLMOps, Responsible AI and platform governance controls covering model abstraction, evaluation, grounding, guardrails, PII protection, tracing, auditability, observability, controlled fallback, and usage and cost management.

Cross-questions

4
A strong answer covers
  • Offline: a versioned golden set on every release, gated in CI with thresholds.
  • Metrics: faithfulness, answer relevance, context precision and recall, citation accuracy, correct refusals.
  • Online: sampled production traces scored by judges, plus user feedback.
  • The tools you used, such as Ragas, DeepEval or Langfuse.
Avoid

“We evaluated manually,” with no repeatable process.

A strong answer covers
  • Detection with Presidio, Comprehend or NER plus patterns, at input and before anything is logged.
  • Reversible tokenization when the answer must contain the original value.
  • Redacted logs, data residency and retention limits.
  • The trade-off: detection recall versus latency and false positives.
Avoid

Treating regex alone as enough.

A strong answer covers
  • Timeouts and retries with jitter, then a circuit breaker opens.
  • Fallback to a second model whose prompts you’ve already tested.
  • Graceful degradation (cached or partial answers) and alerting.
Avoid

Assuming a fallback model behaves the same with the same prompt.

A strong answer covers
  • Per-tenant budgets and token metering.
  • Caching, routing easy queries to smaller models, max-token limits, prompt trimming, batch APIs for offline work.
  • A cost-per-request dashboard reviewed with the business.
Avoid

Not knowing your cost per request.

Your numbers

Interviewers probe numbers first. Write exact values so you never have to improvise.

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