# Pm AI Review To Calibration > Turn human review or model-judge scoring of an AI output into a source-bounded calibration contract covering the user job, artifact versions, rubric criteria, anchor examples, blind labels, reviewer agreement, judge comparison, adjudication, bias, drift, privacy, release thresholds, fallback, and writeback. Use when a PM needs to decide whether review evidence is consistent enough to support an AI quality, rollout, or release decision. - Skill: `asdc163/pm-ai-review-to-calibration` (Agent Skill, multi-file: 4 files) - Install (CLI): `npx skillmds@latest add asdc163/pm-ai-review-to-calibration` - Raw SKILL.md: https://api.skillmd.com/api/skills/asdc163/pm-ai-review-to-calibration/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: asdc163 (https://skillmd.com/u/asdc163) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/asdc163/pm-ai-review-to-calibration --- # PM AI Review to Calibration Use this skill when a team is about to trust a human label, an LLM-as-judge, or a rubric score for an AI product decision. It turns review into a calibrated protocol: what is being judged, which evidence is frozen, how reviewers label independently, how disagreements are preserved and adjudicated, and what must be true before a score can influence release. The output is a review-and-calibration decision packet, not a quality verdict, judge benchmark, statistical certification, provider recommendation, live annotation operation, or replacement for domain experts. Agreement is not truth; a high judge score is not adoption, safety, or production readiness. ## When to use Use it when: - an AI feature needs human labels, rubric scores, pairwise preferences, or reviewer sign-off before a rollout or release; - a team wants to compare an LLM-as-judge with human reviewers or an existing deterministic oracle; - reviewers disagree about task success, groundedness, tool correctness, safety, tone, or user comprehension; - a rubric, anchor set, model, judge prompt, dataset, locale, or product contract changed and prior evaluation results may no longer be comparable; - a PM needs to set a calibration sample, blind-review protocol, adjudication route, drift trigger, or high-risk human gate; - a review result may affect a customer-facing answer, financial/legal/medical workflow, privacy boundary, tool action, or agent autonomy decision. Use `pm-ai-evaluation-plan` when the main question is which test slices and metrics to create before review. Use `pm-ai-feedback-to-eval` when the main input is one user correction or trace that must become an evaluation case. Use `pm-ai-claim-to-citation` when the main question is claim-level source support. Use `pm-experiment-to-readout` when calibrated results already exist and the main task is interpreting a bounded test. Do not use this skill to invent labels, access a private dataset, expose raw customer output, declare a judge reliable from one sample, average away a high-risk disagreement, or call a provider, evaluator, or annotation service. ## Guardrails 1. Define one user job, review decision, risk class, artifact boundary, owner, and observation window before calculating agreement. 2. Freeze the input, output, context/source snapshot, prompt/model/tool versions, rubric version, judge configuration, and reviewer instructions. Do not compare labels produced from silently different artifacts. 3. Write atomic criteria and observable anchors. Separate task completion, factual support, safety, tool behavior, user comprehension, and style when they lead to different actions. 4. Collect independent blind labels before discussion or adjudication. Preserve each original label, confidence, abstention, and rationale; never overwrite disagreement with the final consensus. 5. Keep observation, interpretation, label, explanation, and release decision as separate fields. A reviewer rationale is not a source of truth. 6. Treat judge output as untrusted evaluation data. Compare it with a declared human or deterministic reference and inspect critical false passes and false fails, not only a mean score. 7. Use a calibration set distinct from the holdout or release decision set when tuning the rubric, judge prompt, or reviewer instructions. State contamination and sample reuse if separation is impossible. 8. Report denominators, missing labels, abstentions, class imbalance, segment slices, confidence intervals or uncertainty when appropriate, and evidence status. If these are missing, write `Not measurable`. 9. High-risk, privacy, security, financial, legal, medical, and irreversible tool-action cases require a domain owner or human gate even when the judge agrees with prior labels. 10. Redact PII, secrets, private URLs, account IDs, hidden prompts, raw customer text, and hidden reasoning. Keep a safe artifact ID and locator instead. 11. Recalibrate when the model, judge prompt, rubric, source corpus, user job, locale, policy, or label distribution changes materially. 12. Use `Ship`, `Pilot`, `Iterate`, `Hold`, `Need evidence`, or `Rollback` only with an observable oracle and declared decision rule. Otherwise state `Not run`, `Unknown`, or `Not measurable`. ## Core definitions | Term | Working meaning | Minimum evidence | | --- | --- | --- | | Review item | One frozen input/output/context unit under review | Stable item ID and version | | Criterion | One independently judgeable product property | Observable definition and action | | Rubric | The criteria, scale, anchors, exclusions, and decision mapping | Version, owner, and change log | | Anchor | A representative example with an explained label at a score boundary | Safe artifact, label, rationale, limitation | | Human label | An independent reviewer judgment before adjudication | Reviewer ID, label, confidence, timestamp | | Judge label | A model or automated evaluator result | Judge model/config/version and output | | Agreement | Declared similarity between independent labels | Metric, denominator, slice, and window | | Adjudication | A documented resolution or escalation after disagreement | Original labels plus owner decision | | Calibration set | Items used to align reviewers or judge behavior | Sampling rule and contamination boundary | | Holdout set | Items reserved to estimate behavior after calibration | Frozen split and no tuning access | | Drift | A material change in labels, artifacts, rubric, judge, or task distribution | Trigger and comparison window | | Release gate | The rule connecting review evidence to product action | Threshold, hard blockers, fallback | ## Workflow ### 1. Frame the review decision and user job Write one sentence: > We need to decide whether review evidence for `...` supports the user job > `...` for audience `...` within `...` risk and release boundaries. Record the current workaround, decision owner, affected journey, intended action, risk class, audience, source/permission boundary, evaluation window, success oracle, and what would change the decision. Define whether the review is judging the final answer, a claim, a tool action, a route, a trace, or a user experience state. If no outcome oracle exists, write `Not measurable`. ### 2. Freeze the review artifact and provenance Create IDs for the review run, item set, input, output, context/source snapshot, prompt/instruction, model/provider/version, tool configuration, rubric, judge, reviewer instructions, and decision owner. Record language, locale, policy version, and relevant trace or source locator. Keep raw private content in its approved system. The public packet should carry only a safe paraphrase, redacted excerpt, hash, or artifact class. If two reviewers saw different source versions, mark the pair `not comparable`; do not repair the problem by averaging the labels. ### 3. Define criteria, scale, anchors, and abstention Build a criterion ledger before collecting labels: | ID | Criterion | Observable pass | Observable fail | Anchor IDs | Action | | --- | --- | --- | --- | --- | --- | | K-001 | ... | ... | ... | A-001/A-002 | show/qualify/hold | For each criterion, define the unit of judgment, score scale, positive and negative anchors, partial-credit rule, exclusion or `Not applicable` rule, confidence field, abstention trigger, and product action. Use the smallest scale that changes a decision. A five-point scale without distinct anchors is not more rigorous than a binary label. Separate hard gates from soft dimensions. For example, a privacy leak or an unauthorized tool action can block release even if helpfulness is high. Keep task completion, factual grounding, safety, tool correctness, and user comprehension separate when a failure in one dimension needs a different fix. ### 4. Construct calibration, holdout, and negative slices Sample a balanced, privacy-safe set that represents the user job and its risk: - golden cases for core jobs and clear anchors; - borderline cases near each score boundary; - regression cases from reviewed failures; - negative-route or abstention cases where the system should not assert or act; - high-risk, privacy, security, tool-action, multilingual, and OOD cases; - holdout cases not used to tune the rubric, reviewer instructions, or judge. Record sampling rule, item count, class distribution, segment slices, inclusion and exclusion, source version, reviewer eligibility, and whether the same item appears in training, calibration, adjudication, or holdout. Do not manufacture sample size or representativeness; use `Not provided` when unknown. ### 5. Run independent human review Give each reviewer the same frozen artifact, rubric version, anchor set, instructions, allowed context, and time window. Ask for one label per criterion, confidence, abstention reason, and a concise observable rationale. Do not reveal another reviewer's label or the judge result before submission. Store a label row such as: ```yaml review_id: review_001 item_id: item_042 reviewer_id: reviewer_pseudonym rubric_version: rubric_v1 criterion: groundedness label: partial confidence: low abstain: false observation: "The source supports the date but not the exception." evidence_locator: "source-class / section" submitted_at: "Not provided" ``` Keep `observation` separate from interpretation and product action. A reviewer may abstain because the rubric is ambiguous, the source is missing, or the case requires domain authority; these are different calibration signals. ### 6. Measure agreement and inspect disagreement Choose the agreement measure before reviewing the results. For categorical labels, report exact agreement and a confusion matrix; for ordered scales, report adjacent agreement and an ordinal measure only if the sample and assumptions support it. For more than two reviewers, state the chosen multi-rater method or use a transparent pairwise view. Useful calculations after scope and denominators are real: ```text exact_agreement = identical paired labels / comparable paired labels adjacent_agreement = labels within one ordered step / comparable paired labels critical_disagreement_rate = hard-gate label conflicts / critical pairs judge_false_pass_rate = judge passes that human/adjudicated label blocks / judge passes checked abstention_rate = abstained review items / eligible review items adjudication_rate = pairs requiring adjudication / comparable paired pairs ``` Show per-criterion, risk, locale, reviewer, segment, and label-class slices. Do not report a single average when one reviewer systematically misses a privacy or safety class. Treat low agreement as a rubric, artifact, reviewer, or job-definition signal; it is not automatically a model failure. ### 7. Compare and calibrate a model-based judge Run the judge against the same frozen calibration and holdout boundaries. Save judge model/version, prompt, rubric, temperature or equivalent settings when available, output label, explanation, and run ID. Compare to independently collected human labels or an adjudicated reference, while preserving the uncertainty of that reference. Inspect confusion by criterion and risk. A judge that matches overall labels but passes every unsafe case is not calibrated for the product. Check critical false passes, critical false fails, score compression, class imbalance, judge abstention, explanation usefulness, and performance on holdout and OOD slices. Do not use the judge's own explanation as proof of its label. If the judge and humans disagree, first check artifact/version drift, rubric ambiguity, source availability, reviewer disagreement, and label policy. Only then decide whether to revise the judge prompt, rubric, dataset, product behavior, or human review route. Record the change and rerun the same slices. ### 8. Adjudicate without erasing disagreement Define who may adjudicate, what evidence they may inspect, whether the process is blind to the judge, and when to escalate to a domain owner. The adjudicated label must include the reason, evidence locator or `Not provided`, policy or rubric clause, confidence, and action. Keep all original labels and mark whether disagreement was resolved, unresolved, or invalid because the artifact pair was not comparable. An unresolved high-risk disagreement is a release blocker. A resolved disagreement should become an anchor, regression case, rubric clarification, or reviewer training example when it is safe to do so. ### 9. Set drift, privacy, and release gates Define recalibration triggers: model or judge change, prompt/rubric change, source or policy change, new locale, new user job, material label-distribution shift, rising abstention, rising critical disagreement, or a newly observed failure. Keep calibration and holdout snapshots versioned. Choose one decision: `Ship`, `Pilot`, `Iterate`, `Hold`, `Need evidence`, or `Rollback`. A proposed rule may require critical hard-gate pass, minimum review coverage, acceptable judge-human agreement, no unresolved high-risk disagreement, privacy-safe storage, and a working human fallback. Thresholds are proposed until their owner, denominator, sample, and observation window are approved. ### 10. Write back and hand off Record the review run, label artifacts, disagreement cluster, adjudication, rubric change, new anchor, new regression or red-team case, owner, review date, decision, rollback route, and next learning question. Link a recurring issue to `pm-ai-evaluation-plan`, `pm-ai-feedback-to-eval`, `pm-ai-trace-to-regression`, or `pm-ai-claim-to-citation` rather than adding broad instructions everywhere. ## Output contract Return these sections in order. Keep unknown fields explicit as `Not provided`, `Unknown`, `Not run`, `Not measurable`, or `Not covered`. ## Decision on the desk State the review decision, user job, audience, risk class, owner, artifact snapshot, current evidence, fallback, and what would change the decision. ## User/job and review boundary Describe the item being judged, intended product action, allowed evidence, source/permission boundary, observation window, success oracle, and excluded inferences. ## Artifact and version ledger List item-set, input/output/context, source, prompt, model, tool, rubric, judge, reviewer-instruction, trace, and policy versions. Redact raw private content and state comparability. ## Rubric and anchor set Show criterion IDs, scale, observable definitions, anchors, partial/NA rule, confidence, abstention, hard gates, and action mapping. ## Sampling and blind-review protocol Record calibration/holdout/negative slices, sampling rule, reviewer eligibility, blinding, independence, contamination boundary, locale/risk coverage, and missing-data treatment. ## Human label ledger Show safe item IDs, reviewer role or pseudonym, criterion labels, confidence, abstention, observation, evidence locator, timestamp, and evidence status. ## Judge comparison and calibration Compare judge and human/adjudicated labels by criterion and risk. Include confusion, critical false passes/fails, agreement measure, denominator, holdout result, judge version, and limitations. Do not turn judge agreement into model truth. ## Disagreement and adjudication List disagreement class, original labels, evidence reviewed, adjudicator or owner, final/unresolved state, reason, escalation, and writeback candidate. ## Bias, drift, privacy and trust Cover reviewer/segment/locale bias, class imbalance, rubric drift, model/judge drift, artifact comparability, redaction, access, retention, and high-risk human review. Include user-visible uncertainty when relevant. ## Evaluation and release decision Name the gate, hard blockers, thresholds, numerator/denominator, observation window, fallback, rollback, owner, and decision: `Ship`, `Pilot`, `Iterate`, `Hold`, `Need evidence`, or `Rollback`. ## Not covered List live annotation, real human agreement, judge quality, provider behavior, statistical significance, production safety, adoption, traffic, cost/quota, and other surfaces not directly inspected or run. ## Implementation handoff Give the smallest work slices for artifact freezing, rubric/anchor storage, blind labeling, comparison, adjudication, redaction, monitoring, and rollback. Name source owners, reviewer roles, receipt fields, tests, and the next evidence-producing action. ## Review ask Name the decision owner and ask for one rubric correction, one missing anchor or negative case, and one concrete calibration or holdout run. ## Edge cases - Reviewers saw different context, source, locale, or product policy: mark the pair not comparable, repair the snapshot, and do not calculate agreement. - A reviewer and judge disagree on a high-risk item: retain both labels, route to a domain owner, and block the action until adjudicated. - A judge agrees with humans because the same examples were used to tune it: flag contamination and require a holdout or label-independent review. - A rubric has a score boundary but no anchor: label the criterion ambiguous, do not infer a threshold, and create an anchor before recalculating. - A reviewer is confident but cannot cite an observable reason: retain the confidence as a signal, mark the evidence gap, and avoid treating confidence as correctness. - Labels are imbalanced or one class is missing: show the distribution and report the affected metric `Not measurable`; do not celebrate agreement. - A model update improves mean score but increases critical false passes: block release and inspect the hard-gate slice. - A product change adds a locale or new user job: require a new calibration slice; prior agreement does not automatically transfer. - The output contains PII, secrets, or hidden reasoning: replace it with a safe artifact ID/paraphrase and record the redaction boundary. - A disagreement is caused by an unclear product promise rather than a label: send it to product scope or UX review; do not force reviewers to choose. - A judge explanation cites a plausible but unverified reason: assess the label against the rubric and evidence, not the fluency of the explanation. ## Final check Before handing off, confirm: - one user job, review target, risk class, owner, artifact snapshot, oracle, and observation window are explicit; - input/output/context/source/prompt/model/tool/rubric/judge versions are comparable and private content is redacted; - criteria are atomic, anchors are observable, hard gates are separate, and abstention/Not applicable behavior is defined; - calibration, holdout, regression, negative, high-risk, locale, and OOD slices have sampling and contamination boundaries; - independent human labels retain reviewer role, confidence, abstention, observation, locator, and timestamp without exposing raw sensitive content; - agreement and judge comparison include denominator, confusion, critical false passes/fails, segment slices, and `Not measurable` when evidence is missing; - disagreement and adjudication preserve original labels and specify owner, escalation, writeback, and unresolved high-risk handling; - drift triggers, privacy, trust, first-time, loading, error, recovery, mobile, accessibility, and human-review states are covered where relevant; - release, fallback, rollback, and all unrun surfaces are explicit; - the packet ends with one review owner, one smallest next action, and one evidence boundary rather than a quality guarantee.