AI Eval Regression Debug Workflow

Use this workflow when candidate prompts underperform baseline runs or pass-rates suddenly decline.

Workflow Focus

1. Compare eval outputs
Identify score and pass-rate regressions between baseline and candidate.
Clear delta map of improved and degraded behaviors.
Open Eval Results Comparator
2. Inspect prompt version drift
Review how revisions changed constraints and behavior over time.
Snapshot-level root-cause candidates for regressions.
Open Prompt Versioning + Regression Dashboard
3. Build deterministic regression suite
Generate repeatable tests targeting regression-sensitive cases.
Reproducible QA suite for fixing and retesting.
Open Prompt Regression Suite Builder
4. Regenerate focused test records
Expand edge-case coverage where deltas are most severe.
Higher-confidence regression verification set.
Open Prompt Test Case Generator
5. Finalize risk decision
Aggregate quality, policy, replay, and eval signals for release gating.
Deterministic Block/Review/Ship outcome with actions.
Open AI QA Workflow Runner

Compare baseline and candidate eval runs to quantify score and pass-rate deltas.

Track prompt snapshots, compare constraints, and monitor regression risk before release.

Compare prompt versions, detect removed constraints, and generate deterministic QA suites.

Generate deterministic prompt evaluation cases and JSONL exports for regression testing.

Aggregate AI QA stage metrics into one deterministic Ship/Review/Block release decision.

Score prompt quality, safety, output contract fit, and replay-test risk before release.

Eval Results Comparator vs Prompt Regression Suite Builder

Run-to-run eval delta analysis vs deterministic regression suite construction.

AI QA Workflow Runner vs Eval Results Comparator

End-to-end QA gate decisioning vs baseline-candidate eval delta analytics.

AI QA Workflow Runner vs Prompt Versioning + Regression Dashboard

Final QA stage-gated release decision vs multi-snapshot version drift dashboarding.

Prompt Versioning + Regression Dashboard vs Prompt Regression Suite Builder

Version timeline dashboard monitoring vs focused baseline-candidate regression suite generation.

What is the fastest way to isolate regression root cause?

Start with eval delta comparison, then inspect version drift and rerun targeted deterministic regression suites.

Should release be blocked on any major negative delta?

For critical flows, yes. Large negative deltas should trigger deeper review and retest before promotion to production.

Prompt Release Checklist

Run a practical pre-release prompt QA flow with linting, policy checks, replay testing, and final go/no-go scoring.

RAG Grounding Audit

Tune chunk quality and retrieval grounding with chunk simulation, noise pruning, relevance scoring, and claim-evidence checks.

AI Output Validation

Validate model output format and schema safety for automation pipelines using contract tests and function-call schema checks.