Evaluation & AI Reliability · core
AI Regression Testing
Frozen eval sets, golden cases, rubric versions, stochastic thresholds, canaries, and release gates.
Mental model
AI regression tests compare distributions and task outcomes, not exact strings. Freeze representative cases, version graders, repeat stochastic trials, and gate meaningful deltas. Scope: this card owns the CI application — frozen sets, versioned rubrics, repeat trials against stochastic output, and the threshold that blocks a release. How a grader is built lives in `llm-evals`; what to measure beyond quality lives in `quality-cost-latency-measurement`.
How to study AI Regression Testing
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 LLM Evals: Everything You Need to Know (Hamel Husain), Demystifying Evals for AI Agents (Anthropic), Evaluating the Effectiveness of LLM-Evaluators (Eugene Yan) to check details, but close the source before writing your explanation. Retrieval is the learning step; rereading is only preparation.
Next, compare AI Regression Testing with Prompt & Version Logging, Hallucination & Failure Detection. Ask what changes in correctness, latency, resource use, operability, and failure recovery. Complete Design exercise: AI Regression Testing 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
- LLM Evals: Everything You Need to Know (Hamel Husain) (doc)
- Demystifying Evals for AI Agents (Anthropic) (doc)
- Evaluating the Effectiveness of LLM-Evaluators (Eugene Yan) (doc)
- A Survey on LLM-as-a-Judge (paper)
- Using LLM-as-a-Judge For Evaluation: A Complete Guide (Hamel Husain) (article)
- OpenAI Evals Documentation (doc)
Practice and explain it back
Design exercise: AI Regression Testing
Frozen eval sets, golden cases, rubric versions, stochastic thresholds, canaries, and release gates. Implement designOutline() returning non-empty values for: evalSet, graderVersion, releaseThreshold. Each value must name a concrete mechanism or decision.
Expected evidence: A design outline with evalSet, graderVersion, releaseThreshold plus an explicit failure mode or trade-off.
Open the interactive drill →Review prompts
- How do you set a release gate on a stochastic system without either blocking every release or catching nothing?
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