Mostly controlled
The team can explain the workflow, evidence, ownership, and unresolved risk well enough to keep reviewing internally.
Resource
AI Development Workflow Audit Pre-Audit Checklist
Use this public pre-audit diagnostic to recognize whether AI-assisted development work shows risks the AI Development Workflow Audit is designed to inspect.
The checklist previews the audit lens. It is not a full audit, not a safety guarantee, and not a substitute for evidence-bound review of the actual workflow, repo, validation evidence, and release boundaries.
Pre-audit diagnostic
Why this checklist exists
AI-assisted code can look complete before the workflow around it is trustworthy. This checklist gives teams a first-pass way to notice signals that may justify a paid audit.
The paid AI Development Workflow Audit reviews actual evidence and produces prioritized recommendations. This public checklist only helps you notice whether that kind of review may be useful.
Fit
Who should use this checklist
Use it when AI-assisted development has moved quickly enough that the team is unsure whether the workflow, repo, tests, ownership, or release boundaries are still under control.
It is most useful for founders, technical owners, and small teams preparing to decide whether the audit intake path is appropriate. It is not a broad AI-development advice hub or an implementation guide.
Use
How to use the checklist
Read each category as a signal, not a score. A single weak answer does not prove failure; repeated weak answers show where evidence-bound audit review may be useful.
Work through the categories with the people who own the repo and release decisions. Mark unknowns plainly. The useful output is a clearer audit-fit decision, not a pass/fail certificate.
Checklist categories
Audit-relevant questions to ask before intake
Each category previews a review surface the audit may inspect through actual project evidence.
Workflow control
- Can the team explain how AI-assisted work moves from request to review to acceptance?
- Are review gates explicit enough to prevent accidental acceptance of plausible output?
- Does each change end with a human decision instead of another open-ended prompt?
AI-generated code risk
- Can reviewers identify which code was AI-generated, AI-assisted, or manually edited?
- Are large or mixed-purpose diffs split before they become difficult to inspect?
- Are unsupported assumptions, invented APIs, or silent behavior changes called out?
Task and prompt boundaries
- Was the task narrow enough that a reviewer can tell whether the result stayed in scope?
- Were constraints, excluded files, and acceptance criteria stated before AI assistance began?
- Are follow-up prompts treated as new reviewable work instead of hidden continuation?
Repo and codebase structure
- Does the work follow the existing route, component, API, and data-flow structure?
- Are ownership boundaries still understandable to a future maintainer?
- Has AI-assisted work created duplicate patterns or unclear entry points?
Architecture alignment
- Does the change fit the system's existing architecture instead of introducing a second pattern?
- Are new abstractions justified by actual complexity rather than generated convenience?
- Can the team explain the architectural consequence of accepting the change?
Tests and validation
- Which lint, type, unit, integration, build, or browser checks were actually run?
- Do the checks prove the behavior that matters, or only that the code compiles?
- Are known validation gaps written down before the team trusts the result?
Side effects and integrations
- Does the work alter reads, writes, auth, permissions, provider calls, or user-visible state?
- Are failure states visible without exposing secrets, credentials, or sensitive data?
- Can the team review integration risk without production account access?
Deployment and release boundaries
- Is deployment treated as a separate human-governed decision after validation?
- Are rollout, rollback, config, and environment assumptions documented enough to inspect?
- Could a release decision be delayed without losing track of unresolved risk?
Human review ownership
- Can one accountable human owner make the final judgment on each change?
- Are disagreements, unknowns, and deferred checks visible before acceptance?
- Would another reviewer understand who owns the next decision?
Maintainability and future change risk
- Will the next maintainer understand why the change exists and how to extend it?
- Has AI-assisted work increased hidden coupling, duplication, or route ambiguity?
- Does the team know what should be paused until a structured audit reviews the evidence?
Signals
What the checklist can reveal
The strongest signal is not one bad line of code. It is a pattern showing that the team cannot confidently govern AI-assisted development.
- workflow drift
- AI task-boundary confusion
- architecture drift
- weak validation posture
- test gaps
- unclear human review ownership
- codebase maintainability risk
- repo readiness concerns
- release or deployment boundary risk
- side-effect or integration risk
Limits
What the checklist cannot prove
This checklist cannot prove that a codebase is safe, unsafe, production-ready, maintainable, secure, or ready for deployment.
Those judgments require evidence-bound review. The paid audit inspects submitted context, repo/workflow evidence where appropriate, validation posture, side effects, and human ownership before producing prioritized recommendations.
Audit fit
When to request the audit
A useful pre-audit review should end with a clearer decision about whether the AI Development Workflow Audit is appropriate.
Needs narrower review
The work may be useful, but the evidence is too mixed or broad to judge without smaller reviewable slices.
Audit may be useful
The pattern suggests workflow drift, validation gaps, ownership confusion, or architecture risk that an audit can inspect.
Pause before more AI-assisted work
The team cannot confidently say what is trusted, what is unproven, and who owns the next decision.
Use this checklist before requesting an audit
If the checklist shows workflow drift, weak validation, architecture drift, release-boundary confusion, or unclear human ownership, use the audit intake path to request evidence-bound review.
Boundaries
What this checklist is not
The checklist supports audit readiness. It does not expand the public offer beyond the AI Development Workflow Audit and does not become a second service.
- not a full audit
- not a guarantee that a codebase is safe or unsafe
- not a substitute for evidence-bound review
- not a security certification
- not implementation, repair, rescue, deployment, production fixes, retainer support, or ongoing advisory
- not legal, medical, financial, or regulated professional advice
No secrets
Do not paste private material into public forms
Do not paste secrets, credentials, private keys, service-account JSON, production secrets, regulated data, or proprietary source code into any public form.
- secrets or credentials
- private keys
- service-account JSON
- production secrets
- regulated data
- proprietary source code