Technical Documentation Production

Technical documentation is arguably Claude's highest-ROI application in any organization. The reason is simple economics: documentation is universally acknowledged as important, universally underfunded, and universally hated by the people who write it. Engineers would rather write code. Product managers would rather write PRDs. Nobody wants to write the API reference, the onboarding guide, or the troubleshooting runbook. Claude does not mind.

The production workflow uses the same research-draft-review pipeline from CW-201a, but with domain-specific refinements. The research stage extracts information from code, configs, existing docs, and Slack threads. The draft stage produces documentation in a specific format (API reference, tutorial, runbook). The review stage checks for technical accuracy, completeness, and adherence to your documentation style guide.

The format-specific skills are where the value compounds. An API reference skill knows to extract every endpoint, parameter, response code, and example. A tutorial skill knows to break the workflow into numbered steps with expected output at each step. A runbook skill knows to include prerequisites, step-by-step procedures, rollback instructions, and escalation paths. Each skill encodes the format's requirements so you do not have to specify them in every prompt.

The biggest trap in AI documentation is the hallucinated example. Claude will generate code examples that look correct but do not compile, API calls with parameters that do not exist, and configuration snippets that reference deprecated options. The QA stage must include a compilation or execution check: does the code example actually run? Does the API call return the documented response? Does the config snippet parse without errors? Technical accuracy is non-negotiable in documentation — one wrong example destroys trust in the entire document.