AI tools are generating significant interest in the technical documentation community. The claims range from AI will write all your documentation to AI is useless for technical content. The reality is more nuanced. AI tools can add genuine value in specific documentation tasks — and they can cause real problems when applied without appropriate oversight.
What AI tools are actually being used for
The AI tools most relevant to technical documentation fall into three categories: large language models used for drafting and editing; AI-assisted search and retrieval tools used for knowledge bases and chatbots; and AI-powered translation tools used for localisation.
First drafts from structured inputs
LLMs can produce useful first drafts when given structured inputs: an API specification, a list of parameters, a set of requirements. The draft requires review and editing, but it can reduce the time to a reviewable draft significantly. This works best for reference content where the accuracy can be verified against a source of truth.
Editing and style consistency
AI tools are effective at improving the clarity, consistency, and readability of existing documentation. They can identify passive voice, inconsistent terminology, overly complex sentences, and deviations from a style guide. Used as an editing assistant rather than a primary author, they add value without introducing accuracy risks.
Knowledge base search and retrieval
AI-powered semantic search significantly improves knowledge base usability. Traditional keyword search fails when users describe their problem in different terms than the documentation uses. Semantic search understands intent and retrieves relevant content even when the exact keywords do not match.
Accuracy for technical content
LLMs generate plausible-sounding text, not accurate text. For technical documentation — where accuracy is non-negotiable — every AI-generated statement must be verified against a reliable source. The risk is not that AI produces obviously wrong content; it is that it produces subtly wrong content that passes a casual review.
Building an AI-assisted documentation workflow
The organisations getting the most value from AI in documentation use it for specific, bounded tasks — first drafts from structured inputs, editing assistance, search improvement — with human review at every stage. They do not use AI as a replacement for subject matter expertise or as a way to reduce the time spent on accuracy verification.
AI tools are genuinely useful in documentation workflows — for specific tasks, with appropriate oversight. The organisations that benefit most approach AI with clear-eyed realism: understanding what it can and cannot do, and maintaining the human expertise that ensures accuracy and quality.