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AI Engineering · Version 1.3.0 · Reviewed 2026-08-02

AI Evaluation Set Builder

Make AI behavior measurable and safer for case selection and rubric design with evidence, explicit trade-offs, and a verification plan.

4 method steps 6 documented failure modes 5 diagnostic checks 7 quality gates

Builds labeled evaluation sets with hard cases, adversarial inputs, no-answer cases, and defensible inter-rater agreement.

₹149 one-time

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What this skill helps you do

  • Case selection
  • Rubric design
  • Judge validation

How AI Evaluation Set Builder works

You provide

Production failures, near-misses, and the quality rubric

It inspects

Case-class balance, including refusals and adversarial inputs

It decides

A labeled set with a validated scoring approach

You verify

Judge agreement measured against human labels

What it checks first

AI Evaluation Set Builder builds labeled evaluation sets with hard cases, adversarial inputs, no-answer cases, and defensible inter-rater agreement. Use it when the work involves Case selection, Rubric design, Judge validation.

  1. Layer ordering relative to change frequency, which determines whether the cache is ever reused.
  2. Whether the build is reproducible, or depends on floating tags and network state at build time.
  3. Image provenance and base-image currency, since most container vulnerabilities come from the base.
  4. Whether secrets enter the build context or an intermediate layer, where they persist even if deleted later.
  5. The critical path of the pipeline, distinguished from total pipeline time.

Failure modes it recognizes

  • Copying the entire source before installing dependencies, invalidating the dependency cache on every commit.
  • A secret passed as a build argument and permanently embedded in image history.
  • A `latest` base tag making builds nondeterministic and silently changing runtime behavior.
  • Running as root because the image never declared a user, expanding container escape impact.
  • A cache key that includes a timestamp, so the cache never hits.
  • Parallel jobs sharing a mutable cache and corrupting each other intermittently.

Answers it will reject

  • Adding retries to a flaky pipeline step instead of fixing the nondeterminism, which triples the failure latency.
  • Building images in the same stage as tests, shipping test tooling and credentials to production.
  • Disabling a security scan to unblock a release without recording an exception and an expiry.
  • Optimizing total pipeline duration when the critical path is a single serial step.

Decision rules it applies

  • Order build layers from least to most frequently changed, and copy dependency manifests before source.
  • Use multi-stage builds so the runtime image contains only runtime artifacts.
  • Pin base images by digest for reproducibility and update them deliberately.
  • Never weaken a gate to increase speed; make the gate faster or move it, but keep the signal.

Evidence it asks for

  • Measure per-stage duration and cache hit rate to find where the pipeline actually spends time.
  • Scan the built image and compare findings against the base image to attribute ownership.
  • Verify no secret material exists in image history with a layer inspection.

The method inside

  1. Translate case selection into observable risks and falsifiable acceptance criteria.
  2. Choose the cheapest test level that can expose failures in rubric design.
  3. Add representative positive, negative, boundary, and regression cases for judge validation.
  4. Define deterministic pass/fail signals, ownership, and the release decision when a check fails.

Deliverables

  • Case selection assessment
  • Rubric design decision and action plan
  • Judge validation verification checklist

Evidence requirements

  • Prompts, model/version, tools, retrieval path, and examples
  • Evaluation dataset and failure cases
  • Latency, cost, privacy, and policy constraints

Quality gates

  • Every material claim traces to supplied evidence or is labeled as a hypothesis.
  • The response follows the declared deliverable contract.
  • No execution, access, measurement, or verification is invented.
  • Secrets and personal data are redacted rather than repeated.
  • The user receives a concrete independent verification step.
  • The relevant failure modes in this domain were considered rather than only the reported symptom.
  • No listed anti-pattern was recommended as a solution.

Example task

Input

We want to measure whether our support assistant is improving, but every evaluation we build says it is perfect.

Expected output

A set that always passes is sampling from the easy distribution. Build it from production failures and near-misses, and require at least one class where the correct answer is a refusal. If you use a model as judge, validate the judge against human labels first or you are measuring the judge...

Boundaries and compatibility

Ideal for

  • Case selection: produce a decision or artifact grounded in supplied evidence.
  • Rubric design: produce a decision or artifact grounded in supplied evidence.
  • Judge validation: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Treating prompt text as a security boundary
  • Claiming model quality from a handful of demos

Agent compatibility

  • GitHub Copilot custom agents
  • Claude Agent Skills / SKILL.md
  • Any instruction-following chat model

Tool policy: Advisory by default. No tools are assumed. If the host provides tools, use read-only evidence gathering unless the user explicitly approves a scoped write or execution action.