Documentation Update for AI & ML Engineering

1. Objective

To perform routine Documentation Update within the AI & ML Engineering context.

2. When to use / When not to use

When to use:

• Periodic maintenance.

When not to use:

• During freeze.

3. Inputs (Required/Optional)

Required:

• Current State

Optional:

• Relevant documentation

4. Outputs (Artifacts)

• Updated State: Description of the Updated State artifact.

5. Operating Modes

Mode	Description	Verification Level
Fast	Focus on speed, minimal validation.	Basic syntax/lint checks only.
Standard	Balanced approach.	Unit tests and standard linting.
Deep	Comprehensive analysis and optimization.	Full test suite, performance profiling, security scan.

6. Constraints & Guardrails

• No Broken Builds: Ensure all changes pass the build process.
• Code Style: Strictly adhere to the project's linting and formatting rules.
• Security: Do not introduce new vulnerabilities; sanitize all inputs.
• Performance: Avoid O(n^2) or worse complexity unless strictly necessary and documented.
• Testing: Maintain or improve code coverage; do not degrade it.

7. Procedure

Phase 1: Review

1. Audit AI & ML Engineering assets.
2. Identify stale items.

Phase 2: Action

1. Execute Documentation Update.
2. Verify changes.

Phase 3: Log

1. Update changelog.
2. Notify team.

8. Quality Gates (Definition of Done)

• [ ] code compiles/runs without errors.
• [ ] All new components include identical or improved test coverage.
• [ ] No new linting errors or warnings introduced.
• [ ] Documentation updated (inline and external).
• [ ] Security scan passes (no high/critical severities).

9. Failure Modes & Recovery

Failure Mode	Recovery Action
Build Failure	Check error logs, revert recent changes, verify dependencies.
Test Failure	Isolate failing test, debug logic, or update test if requirements changed.
Linting Error	Run auto-formatter and manually fix remaining issues.
Merge Conflict	Rebase on main, resolve conflicts manually, run tests again.

10. Copy-Paste Prompt

Role: Act as a Lead Engineer.
Task: Execute the Documentation Update for AI & ML Engineering workflow.

## Objective & Scope
- **Goal**: Maintain accuracy and freshness of AI/ML engineering documentation.
- **Scope**: Reviewing current assets, identifying stale content, updating technical details, and verifying accuracy.

## Inputs
- [ ] CURRENT_STATE: Path to existing documentation or repo.
- [ ] CHANGES: List of recent code/infra changes requiring updates.

## Output Artifacts
- [ ] Updated Documentation (Markdown/Wiki)
- [ ] Changelog Entry

## Execution Steps
1. **Review**
   - Audit existing assets against current codebase. Identify discrepancies.
2. **Update**
   - Rewrite outdated sections. Add new examples/diagrams. Deprecate obsolete docs.
3. **Verify**
   - Peer review for technical accuracy. Check broken links.

## Quality Gates
- [ ] No broken links.
- [ ] Code examples match current API.
- [ ] Peer review approved.

## Failure Handling
- If blocked, output a "Clarification Brief" detailing missing info or blockers.

## Constraints
- **Compliance**: Dont leak sensitive internal IPs or secrets in public docs.

## Command
Now execute this workflow step-by-step.