Evaluation & AI Reliability · core
Evidence-backed Verification
Claims, source provenance, executable checks, screenshots, diffs, test outputs, and acceptance criteria.
Mental model
Verification turns an agent claim into inspectable evidence. Match each acceptance criterion to a source, command, artifact, or observed state and keep inference separate.
How to study Evidence-backed Verification
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 RARR: Researching and Revising What Language Models Say, Using Language Models, Enabling Large Language Models to Generate Text with Citations (ALCE), Google Testing Blog — Just Say No to More End-to-End Tests to check details, but close the source before writing your explanation. Retrieval is the learning step; rereading is only preparation.
Next, compare Evidence-backed Verification with Tracing & Replay, Human Review Systems. Ask what changes in correctness, latency, resource use, operability, and failure recovery. Complete Design exercise: Evidence-backed Verification 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: Evidence-backed Verification
Claims, source provenance, executable checks, screenshots, diffs, test outputs, and acceptance criteria. Implement designOutline() returning non-empty values for: acceptanceCriteria, evidence, inferenceBoundary. Each value must name a concrete mechanism or decision.
Expected evidence: A design outline with acceptanceCriteria, evidence, inferenceBoundary plus an explicit failure mode or trade-off.
Open the interactive drill →Review prompts
- What separates evidence from an assertion when an agent reports "the tests pass"?
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