Build note / AI Workflow Audit Kit

From workflow descriptions to inspectable AI experiments

The practical failure mode in AI work is rarely a lack of model capability. It is moving from a promising demo to a public claim without a clear workflow owner, baseline, risk boundary, or eval gate.

The operating question

A useful AI conversation should not begin with "What can the model do?" It should begin with the work: where it happens, who owns it, what input is safe, where judgment is required, and how the result would be checked after a small experiment.

The AI Workflow Audit Kit is a public-safe attempt to make that middle layer visible. It turns synthetic workflow descriptions into AI experiment notes, review gates, outcome checks, and next-step recommendations.

What the kit deliberately avoids

• It does not use employer, customer, or personal data.
• It does not preserve a named person's private style, tone, or identity.
• It does not treat usage volume as proof of value.
• It does not promote a demo unless eval boundaries are clear.

What it tries to prove

The artifact is meant to show a working posture: map the workflow, define the owner, separate human judgment from machine support, build synthetic eval cases, and make the output inspectable.

Current public proof points include 20/20 portable workflow eval cases, 7/7 safety-critical cases, and 6/6 synthetic RAG eval cases with offline deterministic retrieval and optional OpenAI SDK live mode.

Read or run it

The repository is available at github.com/jayjoolee/ai-workflow-audit-kit. The useful entry points are the README, the sample audit, the responsible skill capture protocol, and the RAG eval demo.