Outcome
AI model governance review
A product-led path for risk, compliance, and governance reviewers. Walk the governance learning path, audit source freshness + lifecycle + coverage, run the regression and refusal-boundary suites, and export a Markdown governance review brief paired with a written external testing plan — never a certification.
Outcome headline
Build a defensible governance review with sourced evidence
Problem
- Internal review needs a defensible evidence trail for a model already in production.
- Citations are months old; lifecycle has shifted; the previous brief no longer matches the catalogue.
- The team needs to flag unverified claims and stale sources before approval.
- Reviewers cannot reconstruct the original decision without a sourced trail.
Who this is for
- Risk, compliance, and governance reviewers preparing internal approvals.
- Operators auditing provider coverage and freshness before sign-off.
- Teams maintaining an internal AI inventory with documented data gaps.
What you will produce
Completion is the named Markdown artifacts in your hands — not a certificate, badge, or progress bar.
- Source freshness checklist
- Lifecycle review note
- Data gap list
- Governance review brief
- External test plan
Workflow
5 sequenced steps
Open each step in order. Every route already exists in the product — outcome pages are entry points, not parallel surfaces.
Suggested workflow
Open each step in order. Every route already exists — no parallel UI, no duplicated content.
Walk the governance learning path
Output: Notes on lifecycle, status, benchmarks, pricing.
Open the governance review kit
Open
/kits/governance-review→Output: Sequenced work document with required resources.
Walk the reverification queue
Output: Stale citations flagged for re-read.
Audit coverage
Output: Verified-field counts + citation density per provider.
Export the brief
Output: Markdown governance review brief.
Routes into the product
Each entry opens an existing route. The outcome page is a product entry point, not a parallel surface.
What to learn
Exercises
Lab playbooks
Evaluation prompt sets
What this outcome does not promise
- Compliance certification or approval.
- Legal advice.
- Vendor endorsement.
- Sign-off on behalf of the reviewer's organisation.
What outcome pages do not promise
- No model recommendations, no winner claims, no rankings. Outcome pages route the reader through evidence; the reader's team decides.
- No live pricing, no live status, no fabricated benchmark scores or latency numbers.
- No production-readiness guarantee, no compliance certification, no automation reliability guarantee.
- No SEO ranking guarantees. The outcome label exists so the right team can find the workflow, not as a search promise.
- No accounts, no progress tracking, no course-completion certificates. The artifact list above is the completion signal.