AI Writing Tools¶

Artificial intelligence is transforming technical writing workflows. AI tools can assist with drafting, editing, translation, and research—but they require understanding of both capabilities and limitations to use effectively.

AI in Documentation Workflows¶

AI tools assist various documentation tasks:

Using AI for Drafting¶

Effective Prompting¶

Good prompts produce better results:

Vague prompt:

Write documentation for authentication.

Specific prompt:

Write a getting started guide for developers implementing OAuth 2.0 authentication with our API. Include: - Prerequisites - Step-by-step implementation - Code examples in Python - Common errors and solutions Target audience: Backend developers familiar with REST APIs but new to OAuth.

Iterative Refinement¶

Work with AI iteratively:

1. Generate draft: Use AI for initial content
2. Review for accuracy: Verify technical claims
3. Refine with AI: Ask for specific improvements
4. Edit manually: Apply your expertise and voice
5. Test procedures: Verify any steps work

Example Workflow¶

# Prompt 1: Initial Draft
"Write a procedure for setting up API authentication, including
getting an API key and making the first authenticated request."

# Prompt 2: Refinement
"Add error handling for these scenarios:
- Invalid API key
- Expired key
- Rate limiting"

# Prompt 3: Code Examples
"Add a Python example showing the complete flow with proper
error handling."

# Then: Human review
- Test the code
- Verify error messages match actual API
- Add organization-specific details
- Apply style guide

AI for Editing¶

Grammar and Style¶

AI can suggest improvements:

• Grammar corrections
• Clarity improvements
• Consistency checks
• Tone adjustments

Limitations¶

AI editing has blind spots:

• May not know your style guide
• Can miss technical inaccuracies
• Sometimes "improves" text incorrectly
• May homogenize voice

Best Practices¶

1. Review every suggestion critically
2. Configure AI for your context when possible
3. Maintain human judgment on technical accuracy
4. Preserve intentional style choices

AI for Code Examples¶

Generation¶

AI can generate code examples quickly:

# AI-generated example (requires verification)
import requests

def authenticate(api_key):
    """Authenticate and return access token."""
    response = requests.post(
        "https://api.example.com/auth",
        headers={"X-API-Key": api_key}
    )
    response.raise_for_status()
    return response.json()["access_token"]

Critical Verification¶

Always verify AI-generated code:

• Does it compile/run?: Test the code
• Is it correct?: Does it do what it claims?
• Is it safe?: Any security issues?
• Is it current?: Using up-to-date patterns?
• Is it complete?: All necessary imports, error handling?

Common AI Code Issues¶

• Hallucinated APIs that do not exist
• Outdated library versions
• Missing error handling
• Security vulnerabilities
• Incorrect endpoint URLs

AI for Translation¶

Initial Translation¶

AI provides fast initial translations:

1. Generate translation with AI
2. Review with native speaker
3. Check technical terminology
4. Verify cultural appropriateness
5. Test with target audience

Translation Quality¶

AI translation quality varies:

• Good: Common languages, standard content
• Variable: Technical terminology, domain-specific
• Poor: Idioms, cultural references, nuance

Human Review Required¶

Never publish AI translations without human review:

• Technical accuracy
• Terminology consistency
• Cultural appropriateness
• Natural language flow

AI Limitations¶

Accuracy Issues¶

AI can confidently state incorrect information:

• Hallucinations: Made-up facts, APIs, features
• Outdated info: Training data has cutoff dates
• Confidential confusion: May mix up similar products
• Plausible nonsense: Sounds right but is wrong

Mitigation¶

• Verify all factual claims
• Test all code and procedures
• Cross-reference with authoritative sources
• Be especially careful with specific details (versions, URLs, parameters)

Confidentiality¶

Consider data privacy:

• Do not input confidential information into public AI tools
• Check your organization's AI usage policies
• Consider self-hosted or enterprise AI options
• Be aware of training data usage policies

AI Tools for Documentation¶

General-Purpose AI¶

• Claude: Strong reasoning, long context
• ChatGPT: Broad capabilities, widely used
• Gemini: Google integration, multimodal

Writing-Focused Tools¶

• Grammarly: Grammar and style suggestions
• Wordtune: Sentence rewrites
• Hemingway: Readability analysis

Code-Focused Tools¶

• GitHub Copilot: Code completion and generation
• Amazon CodeWhisperer: Code suggestions
• Cursor: AI-powered code editor

Documentation-Specific¶

• Mintlify Writer: Documentation generation
• Swimm: Code documentation
• Scribe: Process documentation from screen recordings

Responsible AI Use¶

Attribution and Transparency¶

Consider whether to disclose AI assistance:

• Follow organizational policies
• Be honest about AI involvement
• Do not present AI work as purely human-created

Quality Standards¶

AI-assisted content should meet the same standards:

• Accurate and verified
• Well-written and clear
• Properly reviewed
• Tested where applicable

Skill Development¶

AI should augment, not replace, skills:

• Continue developing writing ability
• Learn what AI does well and poorly
• Maintain critical evaluation skills
• Stay current with AI capabilities

Effective AI Collaboration¶

What AI Does Well¶

• Generate initial drafts quickly
• Suggest alternative phrasings
• Create boilerplate content
• Summarize long documents
• Convert between formats
• Generate code examples

What Humans Do Better¶

• Verify technical accuracy
• Understand audience needs
• Make strategic decisions
• Apply organizational context
• Ensure voice consistency
• Test and validate content

Optimal Workflow¶

The most effective approach combines both:

1. Use AI for initial generation and suggestions
2. Apply human expertise for review and refinement
3. Verify accuracy through testing and research
4. Maintain human judgment on final content

Summary¶

AI tools enhance documentation workflows when used appropriately:

• Draft faster with AI assistance
• Always verify AI-generated content
• Maintain human review for accuracy
• Use AI for augmentation, not replacement
• Follow organizational policies on AI use

AI is a powerful tool, but professional technical writing still requires human expertise, judgment, and accountability.

Next: Automation and CI/CD covers automated documentation workflows.