Technical documentation with AI
Technical documentation accompanies a product throughout its entire lifespan, because even years later someone needs to know how it was built and why. It's often neglected because it takes time, and its necessity is underestimated, especially once the project has already been completed.
Every technical trade requires documentation: operating instructions, conformity documentation, technical descriptions. Often it is a legal requirement just to be able to market a product at all. However, if you only fulfil this requirement, you haven't yet tapped the full benefit of clean documentation. It is often only produced at the end of a project, when the budget is tight and minds are already on the next order – with falling motivation, the bare minimum is then put together in a few days, and everything gets filed away. The unanimous opinion is often: too expensive, too time-consuming, unloved, unnecessary.
Modern documentation, however, can do much more. With AI, it pays to start documentation right at the beginning of the project: the ongoing effort drops so much that documentation running alongside the project no longer fails because of budget. The steps and records are continuously handed over to the AI, initially as Markdown files and later as Word and PDF documents. If the AI sets up versioning and structure cleanly, a foundation emerges on which later tasks can build.
This approach counteracts documentation drift, i.e. the gradual divergence between reality and its description. For software development, there are tools that automatically match description and source code. In product development, editorial and PLM systems make managing documents easier, but whether the description still matches the built state has so far had to be checked manually by a person. A sound setup with AI substantially supports the documentation process. When everything is finalised, hardly any gaps remain, because every record has its place from the outset and anything missing is noticed early. You create an almanac of your own product and its history of development. Who did what, when, and what were the implications?
And there is even more: clean records also serve as a basis for lessons learned, so that the same mistakes aren't repeated on the next product – and access to them is a targeted query to the AI instead of days of searching through old folders. "How was it on the last project, do you remember...?" – is a question that no longer needs to be asked. You can quickly and easily look things up via search, by keyword, by date, by component, or by any other criterion.
The machine writes, the human takes responsibility
AI changes what documentation costs: a language model creates a first draft from design data, bill of materials and notes, so that the pure writing work makes up only a small part of the effort. Then an engineer checks: are the details correct, and are all standards complied with? The AI can prepare this check by flagging inconsistencies; responsibility for the result remains with the engineer. The rewriting is then taken over by the machine again, so that the text is spell-checked, cleanly formatted and, on request, available in multiple languages straight away.
What this means for you
Documentation changes to your advantage. It is no longer a tiresome obligation, but serves several purposes at once and speeds up the project. It stays closer to the machine, because an affordable first draft is worthwhile even when only a small detail changes – the description grows along with it. And the terminology stays consistent across all documents.
The effort shifts from writing to checking. The bottleneck lies less often in knowledge than in the hours that go into text processing – the knowledge is usually present in the team; what was missing so far was the time to write it down. Where five technical writers used to be needed, one is now enough with AI support, because the draft is generated from the existing data and only needs to be checked.
Sources
- Aghajani et al. (2019), Software Documentation Issues Unveiled – ICSE 2019. Documentation maintenance is neglected; lack of currency is a common problem.
- Romeo, Raglianti, Nagy, Lanza (2024), Capturing and Understanding the Drift Between Design, Implementation, and Documentation – ICPC 2024. On the drift between implementation and description.
- Xie et al. (2025), How Effective are Large Language Models in Generating Software Specifications? – SANER 2025. AI drafts have their own strengths and weaknesses; expert review remains necessary.
IDEA e.U. · Determined Innovation