What this round tests
- AI eval design — golden sets, regression checks, online metrics
- Hallucination mitigation and confidence calibration
- Cost-and-latency trade-offs at production scale
- Human fallback design when the model fails
What interviewers are listening for
- Concrete eval methodology, not vibes
- Awareness of failure modes (hallucination, prompt injection, data leakage)
- A real plan for cost: tokens, caching, model tiering
Common mistakes
- Treating an LLM as a black-box solution
- Skipping the eval question entirely
- Ignoring cost trade-offs until production
Concepts tested in ai judgment
Practice questions (27)
- Design evals for an AI support assistant
- Eval framework for an AI code-review assistant
- When is human-in-the-loop required?
- Recovering from a viral bad answer
- Latency vs. cost vs. quality on AI chat
- Fallback when the model is uncertain
- Should a startup fine-tune on customer data?
- A new jailbreak goes viral
- Launch criteria for an AI sales assistant
- Evals for an AI K-12 math tutor
- Third-party model vs. self-host
- Eval framework for an AI search product
- Evaluate AI-generated UI components
- First-time-user trust for AI
- Fallback for AI image generation
- Undo for an AI agent action
- Smart routing between AI models
- Caching strategy for an AI assistant
- Lower quality to hit a cost bar
- Hallucinations in a legal AI
- Signal uncertainty to the user
- Label vs. watermark AI output
- Use synthetic data to train
- Design a beta for an AI feature
- Sunset an old AI model version
+ 2 more in the full bank.
Practice this round in PrepOS
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