Language models can make documentation faster to draft, but they do not remove the need for accurate source material, product testing, and editorial review. The most reliable workflow treats the model as a drafting tool rather than the source of truth.
1. Define the reader and the task
Start with the person who will use the document. A setup guide, API reference, troubleshooting article, and architecture decision record each need a different level of context and a different structure.
2. Gather authoritative inputs
Provide the model with the material the document must reflect: current interfaces, code examples, configuration, constraints, known failure modes, and terminology. If the input is incomplete, the draft should mark questions instead of filling gaps with confident guesses.
3. Generate a structured first draft
Ask for an outline before asking for prose. Review the sequence, remove irrelevant sections, and then draft one section at a time. Smaller units are easier to verify and revise than a single long generation.
4. Test every instruction
Run commands, follow links, exercise the described interface, and confirm version-specific behavior. Code samples should be tested in the same environment the reader will use.
5. Edit for clarity
Remove repetition, generic introductions, unsupported claims, and language that hides the actual action. Prefer direct steps, concrete examples, and explicit prerequisites.
6. Keep documentation connected to change
Documentation becomes stale when it is reviewed separately from the product. Link important documents to the code or workflow they describe, and include documentation review in the definition of done for relevant changes.
The useful role for AI
AI is effective at reorganizing source material, generating alternative explanations, identifying missing sections, and producing a draft that an expert can improve. Accuracy still comes from the product, the code, and a reviewer who understands the reader's goal.