AI Evaluation & Observability - AI Engineering

1What AI evals testRecommended startDefines AI evaluation as repeatable measurement of application behavior while preserving deterministic tests for code and contracts.Concept questionEasy~7 minArtificial IntelligenceGenerative AILarge Language ModelsPractice

2Define AI task successTurns a product outcome into observable quality, safety, and operational criteria before selecting convenient scores.Architecture scenarioMedium~10 minArtificial IntelligenceGenerative AILarge Language ModelsPractice

3Build an AI eval datasetCombines production-shaped, expert-authored, edge, and adversarial cases with versioned labels and privacy controls.Architecture scenarioMedium~12 minArtificial IntelligenceGenerative AILarge Language ModelsPractice

4Choose an AI graderMatches exact checks, model judgment, and human expertise to the behavior being measured instead of treating graders as interchangeable.Comparison questionMedium~10 minArtificial IntelligenceGenerative AILarge Language ModelsPractice

5Calibrate an LLM judgeBuilds a focused scoring guide, representative labeled examples, bias checks, and ongoing agreement monitoring for model-based evaluation.Architecture scenarioHard~15 minArtificial IntelligenceGenerative AILarge Language ModelsPractice

6Avoid eval overfittingSeparates development and held-out cases, controls access, detects duplication, and validates improvements on fresh traffic.Concept questionHard~12 minArtificial IntelligenceGenerative AILarge Language ModelsPractice

7Evaluate tools and outputsMeasures schema validity, tool selection, argument correctness, policy, execution outcome, repair, and final user result.Architecture scenarioHard~15 minArtificial IntelligenceGenerative AILarge Language ModelsPractice

8Evaluate grounding and abstentionJudges claims against allowed evidence, separates correctness from support, and tests whether the system answers or abstains appropriately.Architecture scenarioHard~15 minArtificial IntelligenceGenerative AILarge Language ModelsPractice

9Pairwise vs absolute scoringUses pairwise judgments for relative change and absolute criteria for release obligations while controlling order and verbosity bias.Comparison questionMedium~10 minArtificial IntelligenceGenerative AILarge Language ModelsPractice

10Evaluate AI behavior by sliceBreaks results down by meaningful user, task, risk, language, input, and system dimensions instead of trusting one average.Concept questionMedium~10 minArtificial IntelligenceGenerative AILarge Language ModelsPractice

11Set AI release gatesCombines deterministic invariants, severity-aware quality thresholds, slice protection, baseline comparison, and guarded rollout.Architecture scenarioHard~15 minArtificial IntelligenceGenerative AILarge Language ModelsPractice

12Roll out AI changes safelySelects a production experiment based on user exposure, side effects, measurement needs, cost, and rollback risk.Comparison questionHard~15 minArtificial IntelligenceGenerative AILarge Language ModelsPractice

13Trace an AI requestConnects application, model, retrieval, tools, validation, retries, and outcomes while controlling sensitive telemetry.Architecture scenarioMedium~12 minArtificial IntelligenceGenerative AILarge Language ModelsPractice

14Monitor production AI metricsBalances task quality, safety, reliability, latency, tokens, cost, refusals, repairs, and fallbacks by route and slice.Concept questionMedium~10 minArtificial IntelligenceGenerative AILarge Language ModelsPractice

15Use AI product feedbackCombines low-friction ratings, structured reasons, behavioral outcomes, review queues, and privacy-aware eval case creation.Architecture scenarioMedium~12 minArtificial IntelligenceGenerative AILarge Language ModelsPractice

16Detect AI behavior driftSeparates traffic, data, dependency, model, prompt, and grader change while maintaining stable anchors and fresh evaluation.Architecture scenarioHard~15 minArtificial IntelligenceGenerative AILarge Language ModelsPractice

17Diagnose an AI regressionReproduces failing cases, compares versioned traces, isolates the first changed component, mitigates impact, and adds regression coverage.Debugging scenarioHard~15 minArtificial IntelligenceGenerative AILarge Language ModelsPractice

18Design AI quality operationsConnects product success, layered evals, calibrated graders, release gates, guarded rollout, traces, monitoring, feedback, and incident learning.Architecture scenarioHard~20 minArtificial IntelligenceGenerative AILarge Language ModelsPractice