SkillVaultskills Browse all 500 skills

AI Engineering · Version 1.1.0 · Reviewed 2026-08-02

RAG Evaluation Builder

Make AI behavior measurable and safer for retrieval benchmark design and faithfulness grading with evidence, explicit trade-offs, and a verification plan.

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

Builds retrieval and generation evaluations that separate recall, ranking, faithfulness, citation accuracy, refusal, latency, and cost.

₹99 one-time

Get this skill archive

What this skill helps you do

  • Retrieval benchmark design
  • Faithfulness grading
  • Citation evaluation

How RAG Evaluation Builder works

You provide

Build definition, timings, and cache statistics

It inspects

Layer ordering and secret exposure for retrieval benchmark design

It decides

A faithfulness grading change that keeps every gate intact

You verify

Per-stage duration and cache hit rate re-measured

What it checks first

RAG Evaluation Builder builds retrieval and generation evaluations that separate recall, ranking, faithfulness, citation accuracy, refusal, latency, and cost. Use it when the work involves Retrieval benchmark design, Faithfulness grading, Citation evaluation.

  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 retrieval benchmark design into observable risks and falsifiable acceptance criteria.
  2. Choose the cheapest test level that can expose failures in faithfulness grading.
  3. Add representative positive, negative, boundary, and regression cases for citation evaluation.
  4. Define deterministic pass/fail signals, ownership, and the release decision when a check fails.

Deliverables

  • Retrieval benchmark design assessment
  • Faithfulness grading decision and action plan
  • Citation evaluation 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

Our RAG answer quality changes unpredictably when we modify chunking or prompts. Build an evaluation framework.

Expected output

Create a versioned query set with required evidence IDs, measure retrieval recall before generation, then grade claim support and citation correctness separately. One end-to-end score cannot locate regressions...

Boundaries and compatibility

Ideal for

  • Retrieval benchmark design: produce a decision or artifact grounded in supplied evidence.
  • Faithfulness grading: produce a decision or artifact grounded in supplied evidence.
  • Citation evaluation: 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.