Reusable AI capability
Calibrate AI Regression Acceptance Thresholds
Set and maintain risk-based AI regression gates using measurement uncertainty, baseline variance, critical slices, practical significance, and explicit release trade-offs.
This Skill is a reusable set of instructions based on the linked Amo.ng Prompt or Workflow. It tells you what to provide, how to apply the method and how to know when the task is complete. It does not run automatically.
# Calibrate AI Regression Acceptance Thresholds Skill ID: AMO-S-000023 Skill URL: https://amo.ng/skills/calibrate-ai-regression-acceptance-thresholds Purpose: Give evaluation, domain, product, and release owners a repeatable threshold-calibration method that can be reused across versions without treating one aggregate score as the decision. Required inputs: - Exact release claim, affected behaviors, user or risk slices, and decision owner - Baseline and candidate measurements, sample sizes, uncertainty, repeatability, and historical variance - Severity, exposure, practical-significance, guardrail, and non-compensable failure criteria - False-pass and false-block consequences, release constraints, and revalidation cadence How to use: When to use: - AI regression results need a defensible release threshold or an existing gate requires recalibration. - Aggregate pass rates may hide critical slice failures or normal measurement noise. When not to use: - Choosing evaluation tasks or building a dataset from scratch. - Setting thresholds without repeated baseline evidence or a stated decision consequence. Reusable method: 1. State the exact release claim and segment behaviors into material decision slices. 2. Establish baseline distribution, measurement uncertainty, repeatability, and normal variance for each slice. 3. Define practical harm and exposure, not only statistical difference. 4. Set non-compensable gates, acceptable bands, warning bands, and investigation triggers by slice. 5. Test sensitivity to sample size, missing data, multiple comparisons, threshold placement, and false-pass versus false-block cost. 6. Record exceptions, expiry, monitoring, and recalibration triggers; apply the gate only to the version and evidence boundary reviewed. Expected output: A threshold register with baseline evidence, uncertainty, slice gates, non-compensable failures, sensitivity, decision trade-offs, exceptions, owner approvals, and revalidation triggers. Boundaries: Do not invent results, uncertainty, thresholds, approvals, or risk tolerance. Evaluation and domain owners approve measurement interpretation; product and risk owners approve trade-offs; the release owner controls use of the gate. Source: AMO-P-000304. Applicable Workflow: Assure an AI System Change for Production Release. Based on Prompt: Regression Acceptance Threshold Calibration Source ID: AMO-P-000304 https://amo.ng/prompts/regression-acceptance-threshold-calibration Completion criteria: Complete when every material slice has baseline evidence, uncertainty, practical-significance rule, gate and rationale; non-compensable failures are explicit; false-pass and false-block trade-offs are bounded; and each threshold has owner, version scope, expiry or recalibration trigger. Use this Amo.ng Skill with your preferred AI tool. Supply the required inputs and follow the usage instructions. # Calibrate AI Regression Acceptance Thresholds Skill ID: AMO-S-000023 Skill URL: https://amo.ng/skills/calibrate-ai-regression-acceptance-thresholds Purpose: Give evaluation, domain, product, and release owners a repeatable threshold-calibration method that can be reused across versions without treating one aggregate score as the decision. Required inputs: - Exact release claim, affected behaviors, user or risk slices, and decision owner - Baseline and candidate measurements, sample sizes, uncertainty, repeatability, and historical variance - Severity, exposure, practical-significance, guardrail, and non-compensable failure criteria - False-pass and false-block consequences, release constraints, and revalidation cadence How to use: When to use: - AI regression results need a defensible release threshold or an existing gate requires recalibration. - Aggregate pass rates may hide critical slice failures or normal measurement noise. When not to use: - Choosing evaluation tasks or building a dataset from scratch. - Setting thresholds without repeated baseline evidence or a stated decision consequence. Reusable method: 1. State the exact release claim and segment behaviors into material decision slices. 2. Establish baseline distribution, measurement uncertainty, repeatability, and normal variance for each slice. 3. Define practical harm and exposure, not only statistical difference. 4. Set non-compensable gates, acceptable bands, warning bands, and investigation triggers by slice. 5. Test sensitivity to sample size, missing data, multiple comparisons, threshold placement, and false-pass versus false-block cost. 6. Record exceptions, expiry, monitoring, and recalibration triggers; apply the gate only to the version and evidence boundary reviewed. Expected output: A threshold register with baseline evidence, uncertainty, slice gates, non-compensable failures, sensitivity, decision trade-offs, exceptions, owner approvals, and revalidation triggers. Boundaries: Do not invent results, uncertainty, thresholds, approvals, or risk tolerance. Evaluation and domain owners approve measurement interpretation; product and risk owners approve trade-offs; the release owner controls use of the gate. Source: AMO-P-000304. Applicable Workflow: Assure an AI System Change for Production Release. Based on Prompt: Regression Acceptance Threshold Calibration Source ID: AMO-P-000304 https://amo.ng/prompts/regression-acceptance-threshold-calibration Completion criteria: Complete when every material slice has baseline evidence, uncertainty, practical-significance rule, gate and rationale; non-compensable failures are explicit; false-pass and false-block trade-offs are bounded; and each threshold has owner, version scope, expiry or recalibration trigger.Copy Skill copies the full Skill instructions. Use with AI copies those instructions with a short guide for your chosen AI tool. Both actions only copy text—they do not send anything to an AI provider or run the Skill.
Purpose
Give evaluation, domain, product, and release owners a repeatable threshold-calibration method that can be reused across versions without treating one aggregate score as the decision.
Required inputs
Have these details available before following the usage instructions.
- Exact release claim, affected behaviors, user or risk slices, and decision owner
- Baseline and candidate measurements, sample sizes, uncertainty, repeatability, and historical variance
- Severity, exposure, practical-significance, guardrail, and non-compensable failure criteria
- False-pass and false-block consequences, release constraints, and revalidation cadence
How to use this Skill
When to use:
- AI regression results need a defensible release threshold or an existing gate requires recalibration.
- Aggregate pass rates may hide critical slice failures or normal measurement noise.
When not to use:
- Choosing evaluation tasks or building a dataset from scratch.
- Setting thresholds without repeated baseline evidence or a stated decision consequence.
Reusable method:
1. State the exact release claim and segment behaviors into material decision slices.
2. Establish baseline distribution, measurement uncertainty, repeatability, and normal variance for each slice.
3. Define practical harm and exposure, not only statistical difference.
4. Set non-compensable gates, acceptable bands, warning bands, and investigation triggers by slice.
5. Test sensitivity to sample size, missing data, multiple comparisons, threshold placement, and false-pass versus false-block cost.
6. Record exceptions, expiry, monitoring, and recalibration triggers; apply the gate only to the version and evidence boundary reviewed.
Expected output:
A threshold register with baseline evidence, uncertainty, slice gates, non-compensable failures, sensitivity, decision trade-offs, exceptions, owner approvals, and revalidation triggers.
Boundaries:
Do not invent results, uncertainty, thresholds, approvals, or risk tolerance. Evaluation and domain owners approve measurement interpretation; product and risk owners approve trade-offs; the release owner controls use of the gate. Source: AMO-P-000304. Applicable Workflow: Assure an AI System Change for Production Release.
Based on an Amo.ng Prompt
Regression Acceptance Threshold Calibration
Open the linked Prompt to read the instructions this Skill is based on.
Completion criteria
Complete when every material slice has baseline evidence, uncertainty, practical-significance rule, gate and rationale; non-compensable failures are explicit; false-pass and false-block trade-offs are bounded; and each threshold has owner, version scope, expiry or recalibration trigger.
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