Technical documentation is the work engineers hate most and the work that matters most for team scaling. AI does not make engineers love docs — but it removes the friction that causes docs to never get written. Here is the workflow.
1. API documentation from code. AI reads endpoint definitions, generates docs. Engineer refines.
2. README files for new repos/services. Project setup, structure, dev workflow. AI drafts from code; engineer adds context.
3. Architecture Decision Records (ADRs). Engineer describes the decision; AI structures into ADR format.
4. Runbooks for operational tasks. Step-by-step procedures for common ops tasks.
5. Post-mortems and incident writeups. Faster generation from incident timeline + actions taken.
Here is the code for [SERVICE/MODULE]: [PASTE] Write documentation including: 1. Purpose (what this service does) 2. Public interface (functions/endpoints with parameters and return types) 3. Dependencies (what this needs to run) 4. Setup instructions (how to run locally) 5. Common gotchas (based on patterns in the code) 6. Where to find tests Voice: technical but readable. Written for a new engineer who has not seen this code before. Flag anything in the code that is unclear and should be commented before publishing docs.
Docs decay because updating them is friction. AI removes most of the friction — re-generate the doc when the code changes substantially.
Recommended pattern: automated reminder when code in a documented area changes, prompting the engineer to regenerate the affected doc section. Most teams can do this with simple CI checks.