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Reference library

Coding-evaluation methodology updates — checked 7 September 2026

The primer treats references as part of the evaluation system. A source should tell us more than “where an idea came from.” It should identify the claim it supports, the artifact it changes, how fresh it is, and what would make us revisit it.

Primary-source rule

Public claims are re-authored from stable primary evidence wherever possible: official documentation, engineering reports, standards, papers, and source repositories. Research reports and private study notes help us find gaps, but they are not copied into the public primer and do not become evidence by themselves.

For every material claim we record:

  • the exact claim and its scope;
  • the primary source and date checked;
  • the chapter and CX artifact it informs;
  • whether the statement is documented fact, experimental result, proposal, or inference;
  • the owner and review date when freshness matters.

Claim ledger

The maintained claim ledger will use this shape:

Field Purpose
Claim ID and wording Prevents a citation from drifting to support a different statement
Evidence type Separates official behavior, empirical evidence, standards, and local design choices
Source and checked date Makes provenance and freshness visible
CX artifact Connects reading to code, data, grader, policy, or experiment
Confidence and caveat Records what the source does not establish
Revalidation trigger Defines when a model, SDK, API, standard, or practice must be checked again

Seed reference spine

This is the starting collection, not the finished bibliography.

Area Primary reference What it informs here
OpenAI agent runtime OpenAI Agents SDK for Python and Agents SDK quickstart Agent/runner structure, typed tools, traces, and the optional model-backed runtime
OpenAI agent evaluation Agent evals Reproducible agent-quality measurement and trace-oriented evaluation
Anthropic eval design Demystifying evals for AI agents Tasks, trials, graders, transcripts, outcomes, and evaluation workflow
Anthropic agent design Building effective agents Choosing workflow complexity and keeping orchestration understandable
Long-running agent harnesses Effective harnesses for long-running agents Checkpoints, progress preservation, restart/resume evaluation, and durable-state limits
Multi-agent production architecture How we built our multi-agent research system Delegation, coordination, shared context, parallel work, synthesis, and matched-budget evaluation
Skill evaluation OpenAI skill evals Explicit, implicit, competing, stale, and incorrect activation; selection versus execution errors
Tool-agent benchmark τ-bench and its reference implementation Stateful tool interaction, policy adherence, and end-state evaluation
Multi-turn benchmark τ²-bench Multi-turn, dual-control interaction and stateful evaluation
Knowledge and voice agent benchmarking Sierra τ³ and τ-Voice Knowledge-to-action ablations, oracle-context limits, audio timelines, interruptions, and matched text/voice tasks
Dynamic broad behavioral auditing Anthropic Petri and its 2026 stewardship update Simulated users/tools, auditor/judge roles, broad exploration, and lifecycle verification
Targeted behavioral suite generation Anthropic Bloom Behavior-to-scenario generation, diverse rollouts, judgment, seeds, and validation limits
Adaptive multi-turn application tests Giskard multi-turn testing Dynamic simulated users, shared traces, stateful goals, and simulator caveats
LLM judge foundations Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena Judge agreement, bias, limitations, and the need for calibration
Agent benchmark breadth AgentBench Evaluating agents across interactive environments rather than text alone
Trace interoperability OpenTelemetry generative AI semantic conventions Provider-neutral trace naming and observability boundaries
CI enforcement GitHub protected branches Making an eval job a required status check
Progressive delivery Google SRE: Canarying releases Promotion, hold, and rollback decisions using live evidence

How references will grow

New sources enter through a research question or a build decision. They are mapped to one of the nineteen working questions: evaluation units, dataset roles, label hygiene, repeatability, release vectors, offline-to-live validity, harness effects, provider boundaries, evaluator evolution, retrieval, incident promotion, rubric quality, red-teaming, operational bounds, CX outcome definitions, trace localization, multi-turn failure, and overfitting.

Research spine

The larger study and R/Q collection has been normalized into nineteen public questions. The private question bank remains private; each public answer will be written afresh, supported by primary evidence, and proved with a CX artifact.

ID Question the primer must answer CX proof
RQ-01 What is the unit under evaluation? Grade one run at step, trajectory, conversation, and outcome level
RQ-02 Why do capability, optimisation, regression, adversarial, calibration, sealed, and production-shadow datasets differ? Versioned manifests, visibility rules, and promotion history
RQ-03 Which facts can code establish, and which need people or calibrated models? Evaluator-routing table plus known-good and known-bad tests
RQ-04 How do we prevent hidden labels from reaching the agent, simulator, or judge? Separate agent input and evaluator-only oracle, with leakage tests
RQ-05 How many stochastic trials are enough? Repeat policy, reliability metrics, and confidence bounds
RQ-06 How does an evidence vector become block, constrain, canary, expand, or rollback? Versioned policy and reconstructable decision
RQ-07 Do offline evals predict production behavior? Offline-to-canary predictive-validity report
RQ-08 How much does the harness alter the measured result? Controlled runtime and resource-limit ablation
RQ-09 What must the product own, and what can a platform provide? Provider-neutral kernel exercised through two adapters
RQ-10 How does an evaluator change without silently rewriting history? Shadow comparison, calibration report, and evaluator release record
RQ-11 How do retrieval and answer generation fail independently? Retrieval-versus-generation diagnostic matrix
RQ-12 How does a production failure become durable regression coverage? Incident-to-case receipt and coverage-growth metric
RQ-13 What makes a rubric observable and discriminating? Boundary examples, disagreement review, and rubric tests
RQ-14 How do safety evals test the whole system? Threat-linked cases for injection, authorization, privacy, unsafe tools, and escalation
RQ-15 How do latency, cost, retries, and tool errors become non-compensable constraints? Tail-latency, cost-per-success, and gate evidence
RQ-16 How do containment, deflection, verified resolution, adoption, and business value differ? Explicit denominators and quality-adjusted CX outcomes
RQ-17 How do traces localize the failure and critical-path delay? Failure taxonomy, parser, and component attribution
RQ-18 What fails only across a session? Multi-turn, memory, commitment, and perturbation cases
RQ-19 How do criteria, golden cases, prompts, and fixes interact without overfitting? Criteria-before-prompt experiment with held-out evidence

A source remains only if it changes the explanation, implementation, experiment, or decision. The library is curated for depth and auditability, not link volume.

Expanded primary-source map

Measurement, human evaluation, and judges

Topic Primary source Book use
Holistic multi-metric evaluation HELM Scenarios, desiderata, transparency, and limits of one-number evaluation
Human pairwise preference Chatbot Arena Pairwise design, crowdsourced preference, and leaderboard interpretation
LLM-judge behavior MT-Bench and Chatbot Arena judge study Position, verbosity, self-preference, and human comparison
Rubric-guided model evaluation G-Eval Model-based evaluator design and human correlation claims
Open rubric evaluator Prometheus Fine-grained rubric-based grading
Selective judge escalation Trust or Escalate Calibration sets, abstention, coverage, and human escalation
Probability calibration On Calibration of Modern Neural Networks Reliability diagrams, temperature scaling, and calibration error
Generative uncertainty Semantic Uncertainty Meaning-aware uncertainty across generated answers
Extreme-tail reliability Measuring Five-Nines Reliability CEM importance sampling, proposal-support assumptions, estimator efficiency, and the research-to-production boundary
Adaptive/IRT evaluation Computerized adaptive testing for LLM medical benchmarking and Can We Trust Item Response Theory for AI Evaluation? Cost reduction, calibrated item banks, rank stability, population-regime failures, and non-adaptive safety cores
Evaluator meta-evaluation AgentRewardBench Expert-grounded comparison of rule and LLM evaluators for agent trajectories
Corrected judge reporting How to Correctly Report LLM-as-a-Judge Evaluations and No Free Labels Noisy-diagnostic correction, calibration uncertainty, reference quality, and shift assumptions

Behavioral testing, safety, fairness, and alignment

Topic Primary source Book use
Behavioral test design CheckList Capability, invariance, and directional-expectation tests
Automated red teaming HarmBench Standardized behavior and attack evaluation
Jailbreak reproducibility JailbreakBench Threat, attack, defense, and evaluator separation
Over-refusal XSTest Benign near-neighbors and exaggerated safety behavior
Bias in ambiguous QA BBQ Ambiguous/disambiguated bias cases and benchmark limits
Stereotype measurement StereoSet Language-model stereotype evaluation
Toxic degeneration RealToxicityPrompts Naturally occurring prompts and classifier-dependent toxicity measurement
Preference-based instruction following InstructGPT Human demonstrations, preference data, reward models, and policy evaluation
Constitutional alignment Constitutional AI Rule-guided feedback and independent safety evaluation
Reward overoptimization Scaling Laws for Reward Model Overoptimization Proxy optimization and Goodhart-style failure
Hidden-objective auditing Auditing language models for hidden objectives Blind audit games, model organisms, mixed audit methods, and transfer limits
Chain-of-thought monitorability OpenAI monitorability evaluations and internal-workload monitoring disclosure Two-sided monitor evaluation, operational use, fragile observability, and defense-in-depth limits

Interpretability and explanation evaluation

Topic Primary source Book use
Faithfulness versus plausibility Jacovi and Goldberg — Towards Faithfully Interpretable NLP Systems Explicit definitions, graded faithfulness, and causal-evaluation discipline
Attribution sanity checks Adebayo et al. — Sanity Checks for Saliency Maps Model/data randomization controls and protection against visually persuasive false explanations
Attribution-graph evaluation Anthropic — Circuit Tracing methods Interpretability, intervention validation, sufficiency/completeness, graph scope, and limitations

Factuality, retrieval, research, and agents

Topic Primary source Book use
Truthfulness TruthfulQA Misconception-focused truth evaluation and scope limits
Atomic factual precision FActScore Claim decomposition and evidence support
RAG evaluation RAGAS Retrieval/generation dimensions and automated-proxy caveats
General assistant agents GAIA Reasoning, tools, browsing, multimodality, and end-to-end outcome
Browsing agents BrowseComp Obscure fact finding, automatic verification, and narrow-construct caution
Benchmark evaluation awareness Anthropic's BrowseComp investigation Task recognition, leaked-answer retrieval, integrity investigation, and score caveats
Long-form research safety Deep Research system card Prompt injection, factuality, safety, and long-output adaptation
Stateful policy/tool agents τ-bench Tools, policy, environment state, and outcomes
Multi-turn dual-control agents τ²-bench Stateful user-agent interaction and session evaluation
Interactive agent breadth AgentBench Multiple agent environments and scaffolding effects
Factuality suite by evidence mode Google DeepMind FACTS Parametric, search, grounding, and visual factuality as distinct constructs
Repository coding agents SWE-bench Real repository tasks, executable outcomes, and environment validity
Long-term conversational memory LongMemEval Multi-session extraction, updates, temporal reasoning, abstention, and benchmark-to-product limits
Long-context retrieval without lexical cues NoLiMa Latent-association retrieval under long context and stress-test boundaries
Personalization field validity The Inadequacy of Offline LLM Evaluations Real-user evidence for reset-versus-persist evaluation and the offline-to-field gap

Benchmarks and reproducibility

Benchmark or method Primary source Book use
Broad academic knowledge MMLU Narrow construct versus application inference
Diverse capability collection BIG-bench Task breadth, aggregation, and saturation
Graduate-level expert QA GPQA Expert-domain question construction and limits
Mathematical word problems GSM8K Multi-step answer accuracy and reasoning caveats
Executable code correctness HumanEval Test execution and pass@k
Frontier academic questions Humanity’s Last Exam Hard-question capability and expert-verification boundaries
Reproducible evaluation practice Lessons from the Trenches Prompt, implementation, and reporting sensitivity
Evaluator execution boundary Inspect AI security guidance and PaperBench scoring design Model-action sandbox limits, trusted eval code, and fresh scoring state

Production, governance, and operations

Topic Primary source Book use
Deployment-like replay OpenAI deployment simulation Recent production-like context, pre-release failure mining, and forecast validation
Operational automated auditing Anthropic Petri operational update Named pre-deployment/model-assessment use, external adoption, and remaining realism limits
Realtime audio deployment GPT-4o system card and generally available Realtime API Audio-specific pre-deployment evals, live safeguards, and the incomplete end-to-end quality boundary
Evaluation-system validity OpenAI trustworthy third-party evaluations Environment, harness, contamination, budget, and claim disclosure
Model failover Sierra model failover Prevalidated alternatives, failure injection, switching boundaries, and fallback-path evaluation
Online coding-agent evaluation CursorBench Online outcomes, human workload, and failures missed by offline correctness graders
Benchmark integrity investigation Cursor reward hacking investigation Permitted information access, known-fix contamination, and score-integrity controls
Deep-agent evaluation operations How LangChain builds evals for Deep Agents Separating software health from model capability, artifacts, reruns, and operational eval service concerns
Trace semantics OpenTelemetry GenAI conventions Portable telemetry and sensitive-field boundaries
Canary control Google SRE Workbook: Canarying releases Control-relative rollout, analysis, and rollback
AI risk-management profile NIST AI 600-1 Generative AI Profile Risk-to-measurement traceability and lifecycle controls
AI management system ISO/IEC 42001 overview Organizational management-system context
European AI regulation Official EU AI Act portal Starting point for jurisdiction-specific governance review; not legal advice

Open-source implementation map

Official repositories are implementation evidence, not proof that a default metric fits this product:

Source-use rules

  • Benchmark scores from old papers illustrate protocols, not current model rankings.
  • Course duration, access, APIs, licenses, and platform lifecycle are dated claims and must be reverified.
  • Vendor case studies can motivate a pattern but do not establish universal effectiveness.
  • Secondary research identifies topics; public claims should link to primary evidence.
  • A citation supports only the nearby scoped claim, not an entire chapter.
  • Local synthetic results are labeled as synthetic and illustrative.
  • Regulatory mappings require qualified legal and compliance review for the actual jurisdiction and use case.