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Documentation Workflows: How We Cut Doc Time by 60%

• by Alien Brain Trust • AI Learning
Documentation Workflows: How We Cut Doc Time by 60%

Documentation Workflows: How We Cut Doc Time by 60%

Meta Description: AI cut our documentation time by 60% (22 hours saved in 90 days). Here’s the template-driven workflow and quality checklist that actually works.

Nobody likes writing documentation. It’s tedious. It gets outdated immediately. Developers skip it until code review forces their hand.

And yet, good docs save hours of onboarding time, prevent bugs, and make code maintainable.

AI changes the economics of documentation. We saved 22 hours on docs in 90 days—a 60% reduction in doc time. More importantly, our docs got better. More complete. More accurate. More useful.

Here’s the template-driven workflow that made it happen.

The Data: 22 Hours Saved on Documentation

From our 90-day experiment:

Task TypeCountHours SavedAvg/TaskQuality
API documentation129 hours0.75 hrsPositive
README files86 hours0.75 hrsPositive
Inline code comments74 hours0.57 hrsNeutral to positive
Technical specs43 hours0.75 hrsPositive
Total3122 hours0.71 hrsPositive overall

Key insight: AI saved 0.7 hours per documentation task. That’s not huge per task, but it adds up. More importantly, docs that would have been “TODO” actually got written.

Quality impact: Positive across the board. Why? Because AI doesn’t skip sections or leave TODOs. It completes the template every time.

The Template-Driven Approach

Here’s the framework that cut doc time by 60%:

Step 1: Create Templates for Common Doc Types

We built templates for our 4 most common doc types:

Template 1: API Documentation

# [Endpoint Name]

## Purpose
[What this endpoint does in one sentence]

## Endpoint
`[METHOD] /api/path`

## Authentication
[Required auth: API key, JWT, etc.]

## Request Parameters
| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| param1 | string | Yes | [Description] |

## Request Example
```json
{
  "example": "request"
}

Response Format

{
  "example": "response"
}

Error Codes

CodeMeaningResolution
400Bad Request[How to fix]

Rate Limits

[Limits if applicable]

Notes

[Edge cases, gotchas, security considerations]


#### Template 2: README Files
```markdown
# [Project Name]

## What This Does
[One paragraph: what problem does this solve?]

## Quick Start
```bash
# Installation
npm install

# Configuration
cp .env.example .env
# Edit .env with your settings

# Run
npm start

Configuration

VariableRequiredDefaultDescription
VAR_NAMEYes-[What it does]

Usage Examples

[Common use cases with code examples]

Architecture

[High-level overview: how does it work?]

Development

# Run tests
npm test

# Run linter
npm run lint

Troubleshooting

IssueSolution
Common error 1How to fix

Security Considerations

[Auth, secrets, permissions, etc.]

Contributing

[How to contribute if applicable]

License

[License info]


#### Template 3: Code Comments

// Function: [name] // Purpose: [What it does in one sentence] // Parameters: // - param1 (type): [Description] // Returns: [What it returns] // Throws: [Errors it can throw] // Example: // const result = functionName(arg1, arg2) // Notes: [Edge cases, gotchas, performance considerations]


#### Template 4: Technical Specs
```markdown
# [Feature Name] Technical Specification

## Overview
[What we're building and why]

## Requirements
- Functional requirement 1
- Functional requirement 2

## Architecture
[How it will work - components, data flow]

## API Changes
[New endpoints, modified endpoints, breaking changes]

## Database Changes
[New tables, schema changes, migrations]

## Security Considerations
[Auth, validation, data protection]

## Testing Strategy
[Unit tests, integration tests, manual testing]

## Deployment Plan
[How to roll this out safely]

## Rollback Plan
[How to undo if it breaks]

## Timeline
[Milestones without specific dates]

## Open Questions
[What's still unclear?]

Step 2: Use AI to Fill the Template

Old workflow (manual):

  1. Open blank doc
  2. Try to remember what sections to include
  3. Write some sections, skip others
  4. Get distracted, save as TODO
  5. Never finish it

Time: 30-60 minutes per doc (often incomplete)

New workflow (AI-assisted):

  1. Pick the appropriate template
  2. Give AI the code/feature to document
  3. AI fills in the template completely
  4. Human reviews for accuracy and adds context
  5. Done

Time: 10-20 minutes per doc (always complete)

Real Example: API Documentation

Task: Document a new /api/users/search endpoint

AI Prompt:

Using the API documentation template, document this endpoint:

[Paste the code for the search endpoint]

Include:
- All query parameters
- Response format
- Error codes
- Rate limiting (100 requests/minute)
- Security note about PII in results

AI Output (in 30 seconds):

# User Search API

## Purpose
Search for users by email, name, or user ID with optional filtering.

## Endpoint
`GET /api/users/search`

## Authentication
Requires JWT token in Authorization header: `Bearer <token>`

## Request Parameters
| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| q | string | Yes | Search query (email, name, or ID) |
| limit | integer | No | Results to return (default: 20, max: 100) |
| offset | integer | No | Pagination offset (default: 0) |
| role | string | No | Filter by role (admin, user, guest) |

## Request Example
```bash
GET /api/users/search?q=john@example.com&limit=10&role=admin

Response Format

{
  "results": [
    {
      "id": "usr_123",
      "email": "john@example.com",
      "name": "John Doe",
      "role": "admin",
      "created_at": "2026-01-01T00:00:00Z"
    }
  ],
  "total": 42,
  "limit": 10,
  "offset": 0
}

Error Codes

CodeMeaningResolution
400Missing or invalid query parameterInclude ‘q’ parameter
401UnauthorizedProvide valid JWT token
403ForbiddenUser lacks permission to search
429Rate limit exceededWait 60 seconds before retrying

Rate Limits

100 requests per minute per API key

Notes

  • Results include PII (email, name). Ensure proper authorization checks.
  • Search is case-insensitive and uses fuzzy matching
  • Maximum query length: 100 characters
  • Deleted users are excluded from results

**Human review (5 minutes):**
- Verify response format matches actual code
- Add note about specific PII handling requirements
- Confirm rate limit implementation
- Done

**Time saved:** 25 minutes (35 min manual → 10 min AI-assisted)

## The Quality Checklist for AI Docs

AI-generated docs need human review. Here's our checklist:

### Accuracy Checks
- [ ] Code examples actually work (copy-paste and test)
- [ ] Parameter types match the actual code
- [ ] Response format matches what the API returns
- [ ] Error codes are correct and complete
- [ ] Edge cases are documented (not hallucinated)

### Completeness Checks
- [ ] All required sections are filled in
- [ ] No TODOs or placeholders left
- [ ] Security considerations included
- [ ] Error handling documented
- [ ] Examples cover common use cases

### Clarity Checks
- [ ] Purpose is clear in one sentence
- [ ] Technical jargon is explained or avoided
- [ ] Examples are realistic (not `foo`, `bar`, `baz`)
- [ ] Instructions are actionable
- [ ] Troubleshooting covers actual issues

### Security Checks
- [ ] Auth requirements clearly stated
- [ ] PII/sensitive data handling documented
- [ ] Rate limits and abuse prevention mentioned
- [ ] Secrets/credentials are NOT in examples
- [ ] Permissions and access control explained

**Rule:** Every AI-generated doc goes through this checklist before publishing.

## When AI Helps (and When It Hurts)

After 31 documentation tasks, here's what works:

### AI Excels At:
✅ **Structured documentation** - API docs, README files, specs with templates
✅ **Extracting info from code** - Reading functions and documenting parameters
✅ **Generating examples** - Code samples, request/response formats
✅ **Completing tedious sections** - Error codes, parameter tables, configurations
✅ **Consistency** - Same format across all docs

### AI Struggles With:
❌ **Why something exists** - Business context, decision rationale
❌ **Gotchas from experience** - "This breaks if you do X" (unless it's in the code)
❌ **Audience adaptation** - Doesn't know if readers are beginners or experts
❌ **Prioritization** - What's important vs. nice-to-have
❌ **Metaphors and explanations** - Technical concepts explained simply

### The Decision Framework:

Is the content structured and extractable from code? ├─ YES → Use AI with template, review for accuracy └─ NO → Write manually or give AI extensive context


## Real Examples: What Worked and What Didn't

### Success 1: API Documentation (9 hours saved)

**What we did:** Created API doc template, had AI document 12 endpoints
**Time:** 10-15 minutes per endpoint (vs. 45-60 minutes manual)
**Quality:** Excellent. Docs were complete, accurate, and consistent
**Why it worked:** APIs have structure. AI excels at structured content.

### Success 2: README Files (6 hours saved)

**What we did:** README template for new projects, AI fills it in
**Time:** 15-20 minutes per README (vs. 45-60 minutes manual)
**Quality:** Good. Required human review for "why this exists" sections
**Why it worked:** Templates enforce completeness. AI doesn't skip sections.

### Failure 1: Architectural Decisions (ADRs)

**What we tried:** Have AI write Architecture Decision Records
**Result:** Generic, unhelpful. Missed the "why" entirely.
**Lesson:** ADRs require business context AI doesn't have. Humans must write these.

### Failure 2: Tutorial-Style Docs

**What we tried:** AI writes a tutorial for new developers
**Result:** Technically accurate but pedagogically weak. Wrong order, missed key insights.
**Lesson:** Teaching requires understanding the learner's journey. AI can't do that yet.

## The 4-Step Documentation Workflow

Here's the process we use daily:

### Step 1: Classify the Doc Type (30 seconds)
- Is it API docs? → Use Template 1
- Is it a README? → Use Template 2
- Is it code comments? → Use Template 3
- Is it a spec? → Use Template 4
- Is it something else? → Maybe write manually

### Step 2: Provide Context to AI (2 minutes)

Using [Template Name], document [code/feature]:

[Paste relevant code or describe the feature]

Special requirements:

  • [Security considerations]
  • [Rate limits or constraints]
  • [Edge cases to mention]

### Step 3: AI Generates Draft (30-60 seconds)
AI fills in the template completely

### Step 4: Human Review and Enhance (5-10 minutes)
- Run the quality checklist
- Test code examples
- Add "why" context AI can't know
- Clarify anything confusing
- Publish

**Total time:** 10-15 minutes per doc
**Manual time:** 30-60 minutes per doc
**Savings:** 60% average

## Security Considerations for AI-Generated Docs

Documentation can leak sensitive information. Our security checklist:

### Before Generating Docs:
- [ ] Remove or redact sensitive code before giving to AI
- [ ] Don't include actual API keys, secrets, or credentials
- [ ] Sanitize database schemas (remove sensitive column names)
- [ ] Redact internal service names if they're sensitive

### After AI Generates Docs:
- [ ] Verify no secrets in code examples
- [ ] Check that error messages don't leak system info
- [ ] Ensure auth requirements are clear
- [ ] Validate that PII handling is documented correctly
- [ ] Confirm rate limits and security controls are mentioned

**Rule:** Docs go through security review just like code.

## Practical Implementation Guide

Want to adopt this workflow? Here's the 30-day plan:

### Week 1: Build Templates
- Identify your 3-5 most common doc types
- Create templates with all required sections
- Document what goes in each section
- Test templates on 2-3 existing docs

### Week 2: AI-Assist Existing Docs
- Pick 5-10 incomplete or outdated docs
- Use AI to complete them using templates
- Review for accuracy
- Measure time saved vs. manual

### Week 3: Make It the Default
- Require templates for all new documentation
- Train team on AI-assisted workflow
- Establish review checklist
- Track compliance and quality

### Week 4: Refine and Scale
- Gather feedback on template quality
- Adjust templates based on what works
- Create additional templates for edge cases
- Document the documentation workflow (meta!)

**Timeline:** Most teams see measurable improvements in 30 days.

## The Bottom Line

22 hours saved on documentation in 90 days. 60% reduction in doc time. Better quality docs.

But the real win isn't time saved—it's **docs that actually exist**.

Before AI: Half our code had no docs. Writing docs felt like punishment.

After AI: Documentation is fast enough that we actually do it. Templates ensure nothing gets skipped.

The question isn't "Should we use AI for docs?"

It's: **"Can we afford to keep writing docs manually?"**

When AI can fill a template in 30 seconds and humans just review for accuracy, the economics change completely.

Documentation stops being a chore. It becomes a 10-minute task you actually complete.

That's the leverage.

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**Next in this series:** Post 6 covers content creation workflows—the 3-pass editing system (draft → refine → humanize) that maintains your brand voice while saving time.

**Try this today:** Create a template for your most common doc type. Use AI to fill it in for one piece of code. Review it. You'll see the time savings immediately.