Prompt Regression Suite Skill

Every prompt tweak, model upgrade, and context change is a deploy. This skill designs the suite that runs on each one and answers a single question: did anything that used to work stop working?

What This Skill Produces

• A golden case set: curated inputs with per-case pass criteria
• Scoring methods per case class (exact, rubric-judge, property checks)
• CI gate thresholds — what blocks a merge vs. what warns
• A failure triage protocol — flaky vs. regressed vs. golden-set-wrong

Required Inputs

Ask for (if not already provided):

• The feature and its contract — what the LLM step receives and must produce
• What has broken before (or nearly) — past incidents seed the best cases
• Real traffic examples — 10-20 representative inputs, including ugly ones
• What triggers a run — prompt edits, model bumps, retrieval changes, all of the above?

Building the Golden Set

Compose the set from four deliberate classes — not a random sample:

Keep it small enough to run on every change (30-80 cases beats 500 nobody runs). Version it in git next to the prompt.

Scoring Per Case

Choose the cheapest check that catches the regression:

1. Exact / structural — JSON parses, required fields present, enum values legal. Free and deterministic; use wherever the contract is structural.
2. Property checks — output contains/never-contains X, length bounds, citation count. Deterministic proxies for quality.
3. LLM-as-judge with a rubric — only where judgement is unavoidable. Pin the judge model + rubric version, score against the baseline output, and spot-check judge agreement with a human on ~20 cases before trusting it.

Every case records: input, pass criteria, scoring method, and the baseline output at the time it was added.

CI Gates

• Block the merge: any past-failure or canary case fails; structural pass rate < 100%; overall pass rate drops more than [X]% vs. baseline.
• Warn, don't block: judge-scored quality drifts within tolerance; latency/cost moves past its soft budget (pair with llm-cost-latency-budget).
• Every run logs model ID, prompt version, and per-case results — regressions must be diffable to the exact change.

Failure Triage Protocol

When a case fails, classify before "fixing":

1. Flaky — re-run N times; if intermittent, tighten the prompt/temperature or the check, don't ignore it.
2. Genuine regression — the change made it worse: revert or fix the change.
3. Golden set wrong — the new behaviour is actually better: update the case via review, never silently, and record why the expectation changed.

Output Format

Prompt Regression Suite: [feature]

Trigger: runs on [prompt edit / model bump / retrieval change] via [CI job].

Golden set ([n] cases):

Gates: merge blocks when [conditions]. Warnings on [conditions].

Triage: [the three-way protocol, with who owns updates to the golden set]

Maintenance: every production incident adds a case within [period]; the set is reviewed for dead cases each [quarter].

Quality Checks

• Every past production failure appears as a permanent case
• Canary cases cover the behaviours that must never change (refusals, format, safety)
• No case relies on an LLM judge where a structural or property check would do
• Gate thresholds are numbers, not "significant degradation"
• The suite is fast and cheap enough that it actually runs on every change — state its runtime and cost

Anti-Patterns

• Do not test only happy paths — the suite exists for the inputs that hurt you
• Do not let anyone update golden expectations in the same PR that broke them, without review
• Do not use an unpinned judge model — a judge that upgrades itself moves your baseline silently
• Do not treat pass-rate-vs-baseline as the only gate — one dead canary matters more than 2% aggregate drift
• Do not grow the set unboundedly — a suite too slow to run on every change protects nothing