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AI Engineering · Version 1.2.0 · Reviewed 2026-08-02

LLM Prompt Regression Detector

Make AI behavior measurable and safer for baseline pinning and regression detection with evidence, explicit trade-offs, and a verification plan.

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

Detects quality regressions across prompt, model, and retrieval changes using pinned evaluation baselines.

₹149 one-time

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What this skill helps you do

  • Baseline pinning
  • Regression detection
  • Change attribution

How LLM Prompt Regression Detector works

You provide

Suite structure, failure history, and the risk to cover

It inspects

Nondeterminism sources affecting baseline pinning

It decides

A regression detection plan at the cheapest useful level

You verify

The test fails when the behavior is broken, not only passes

What it checks first

LLM Prompt Regression Detector detects quality regressions across prompt, model, and retrieval changes using pinned evaluation baselines. Use it when the work involves Baseline pinning, Regression detection, Change attribution.

  1. Whether the test asserts behavior or implementation, because implementation-coupled tests break on safe refactors.
  2. Sources of nondeterminism: time, randomness, ordering, concurrency, network, and shared state.
  3. Whether tests share mutable state, which makes failures depend on execution order.
  4. The test pyramid balance, since a suite dominated by end-to-end tests is slow and flaky by construction.
  5. Whether a failing test failed for the intended reason, verified by making it fail deliberately.

Failure modes it recognizes

  • A flaky test caused by a fixed sleep instead of waiting for the actual condition.
  • Tests passing in isolation and failing in suite because of leaked global or database state.
  • Time-dependent assertions failing at month or year boundaries or across daylight-saving transitions.
  • Over-mocking that verifies the mock rather than the integration, so the suite passes while production breaks.
  • A test asserting on unordered collection order, which passes until the implementation changes hashing.
  • Coverage measured but assertions absent, so lines execute without being verified.

Answers it will reject

  • Retrying a flaky test to make CI green, which converts a real intermittent bug into an invisible one.
  • Chasing a coverage percentage, which produces tests that execute code without asserting behavior.
  • Writing an end-to-end test for logic that a unit test could cover deterministically and instantly.
  • Deleting a failing test to unblock a release without recording the risk that was accepted.

Decision rules it applies

  • Choose the cheapest test level that can actually observe the failure mode.
  • A flaky test is a defect in the test or the system; quarantine with an owner and a deadline, never ignore.
  • Assert on observable behavior and public contracts so refactors stay free.
  • Every bug fix gets a test that fails before the fix and passes after it.

Evidence it asks for

  • Run the suite in randomized order to expose inter-test dependencies.
  • Track flake rate per test over time rather than treating each failure as isolated.
  • Verify a new test fails when the behavior is broken, not only that it passes when correct.

The method inside

  1. Establish what is actually true about baseline pinning from the supplied evidence, and mark what is missing.
  2. Identify the mechanism behind regression detection rather than restating the symptom.
  3. Choose the smallest defensible change for change attribution, weighing impact, confidence, effort, and reversibility.
  4. Measure quality, safety, latency, and cost separately

Deliverables

  • Baseline pinning assessment
  • Regression detection decision and action plan
  • Change attribution 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 assistant quality dropped last week and we changed the prompt, the model version, and the retriever.

Expected output

Three simultaneous changes make attribution impossible, which is the real problem to fix. Pin all three, re-run a labeled set against each combination, and adopt a policy of one variable per change so the next regression takes minutes rather than days...

Boundaries and compatibility

Ideal for

  • Baseline pinning: produce a decision or artifact grounded in supplied evidence.
  • Regression detection: produce a decision or artifact grounded in supplied evidence.
  • Change attribution: 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.