Evaluation & AI Reliability · core
Human Review Systems
Review queues, risk routing, disagreement, calibration, escalation, auditability, and learning from corrections.
Mental model
Human review is a risk-control system. Route uncertain or high-impact cases, give reviewers evidence and clear rubrics, measure agreement, and feed corrections into evals.
How to study Human Review Systems
Begin by restating the mental model in your own words, then connect it to a concrete system you have built or operated. Name the mechanism, the constraint it addresses, and the trade-off it introduces. Use People + AI Guidebook — Feedback + Control (Google PAIR), People + AI Guidebook — Errors + Graceful Failure (Google PAIR), NIST AI Risk Management Framework 1.0 (NIST AI 100-1, PDF) to check details, but close the source before writing your explanation. Retrieval is the learning step; rereading is only preparation.
Next, compare Human Review Systems with Evidence-backed Verification. Ask what changes in correctness, latency, resource use, operability, and failure recovery. Complete Design exercise: Human Review Systems and preserve the command, input, output, and one failed attempt as evidence. Finish by explaining the idea without jargon to someone who has not studied the track.
Proof of understanding
- Explain the mechanism from first principles and identify the state it reads or changes.
- Give one situation where the concept is the right choice and one where it is not.
- Predict a realistic failure mode before running the drill, then compare the prediction with evidence.
- Connect the result to a roadmap or build artifact instead of treating the concept as isolated trivia.
Learn from primary sources
Practice and explain it back
Design exercise: Human Review Systems
Review queues, risk routing, disagreement, calibration, escalation, auditability, and learning from corrections. Implement designOutline() returning non-empty values for: riskRouting, reviewRubric, feedbackLoop. Each value must name a concrete mechanism or decision.
Expected evidence: A design outline with riskRouting, reviewRubric, feedbackLoop plus an explicit failure mode or trade-off.
Open the interactive drill →Review prompts
- Review capacity is finite. What decides which cases reach a human, and what do you measure about the reviewers themselves?
Build evidence
Synthesize: Evaluation & AI Reliability
Build an evidence-backed evaluation and observability system for models, tools, and agents. Produce one working system, benchmark, or evidence-backed design that integrates the path.
- Implements or precisely models the core mechanisms from all three milestones
- Includes at least one injected failure or adversarial case and demonstrates recovery
- Reports quality, latency, resource, reliability, or usability measurements relevant to the domain
- Ships a concise architecture note explaining decisions, trade-offs, and remaining risks