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AI for Technical Writing

5 min read
Tech WriterDevrel

Tech Writer

AI drafts fast. You own accuracy, tone, and 'will a developer actually understand this?'

Devrel

Use AI for tutorial outlines and code samples. Your authenticity and community knowledge are irreplaceable.

AI for Technical Writing

TL;DR

  • AI excels at drafting structure, boilerplate, and first passes. It struggles with accuracy and voice.
  • Technical docs require precision. Every claim and code sample must be verified.
  • Use AI to compress the boring parts. You add the "someone will actually use this" layer.

Technical writing is detail work. AI can produce a lot of text quickly. The risk: wrong details, wrong tone, and docs that read like they were written by a robot (because they were).

API References and Specs

Good use cases:

  • "Generate OpenAPI/Swagger from this code" (as a starting point)
  • "Draft parameter descriptions for these endpoints"
  • "Create example requests and responses"

Cautions:

  • AI will invent parameters, status codes, or behavior. Verify against actual API.
  • Version drift — docs must match the deployed API. AI doesn't know your version.
  • Examples — run them. AI's examples often have typos or wrong assumptions.

Workflow: Generate → verify against spec or code → fix. Never publish unverified.

Tutorials and How-Tos

Good use cases:

  • "Outline a tutorial for [topic]. Audience: intermediate devs. Steps: 5-7."
  • "Draft the intro and setup section"
  • "Create a troubleshooting section for common errors"

What you add:

  • Actual steps that work (AI will skip steps or assume knowledge)
  • Screenshots and diagrams (AI can't create these)
  • "Why" and "when to use this" — context AI doesn't have
  • Your voice — tutorials that feel human get better feedback

Changelogs and Release Notes

Good use cases:

  • "Turn these commit messages into a changelog"
  • "Draft release notes. Tone: professional, highlight breaking changes"
  • "Summarize this diff for a release note"

Cautions:

  • AI may over-dramatize or under-state. "Minor fix" vs. "Critical security patch" — you decide.
  • Don't let AI write release notes for security issues without careful review.

Accuracy and Verification

The golden rule: if it's in the doc, you own it. AI can be wrong. Readers assume the writer verified. So verify.

  • Code samples: run them.
  • Claims about behavior: test them.
  • Version numbers, links, dates: check them.

Maintaining Voice

AI tends toward generic, enthusiastic, or corporate. Technical docs often need: direct, concise, occasionally dry. Add personality where it helps. Strip fluff. "In order to" → "To." "It is important to note" → cut it.

You ask AI: 'Write API docs for our endpoints.' AI outputs 20 pages. You publish. A developer finds wrong parameter names, a deprecated status code, and an example that doesn't run. Trust damaged.

Click "AI draft → verify against source → ship" to see the difference →

Quick Check

AI generates API documentation with code examples. What must you do before publishing?

Do This Next

  1. Have AI draft one doc (API description, tutorial intro, or changelog). Edit it for accuracy and voice. Note the delta.
  2. Create a style prompt for your docs (tone, terminology, structure). Use it as a custom instruction. See if output improves.