I asked an AI assistant to generate the API reference documentation for a new endpoint, giving it the function signature and a brief description. It produced clean, readable documentation in about twenty seconds. It also invented two parameters that didn’t exist, gave one parameter the wrong type, and described the return value in a way that was plausible but incorrect for this specific system.
The output looked right. It wasn’t.
This is an accurate picture of where AI tools stand for technical writers right now. They’re useful for specific tasks and unreliable for others, and the line between the two is roughly the line between generating plausible text and generating accurate text.
AI does well with first drafts from structured inputs. If you have a set of code comments, an internal spec document, or notes from an engineering meeting, an AI assistant can turn those into a readable first draft faster than you can write one from scratch. That draft will need editing, verification, and restructuring, but starting from something is genuinely faster than starting from nothing, especially for content types where the structure is predictable: release notes, installation steps, parameter descriptions with known shapes.
AI also helps with editing tasks that are mechanical rather than substantive. Checking for passive voice, flagging jargon, generating alternative phrasings for sentences that feel awkward: these are tasks where the AI can work through a document quickly and flag things for a human to evaluate. It doesn’t replace the editor’s judgment, but it moves the mechanical work faster.
What AI does poorly is understand context. Knowing what’s actually true about a specific system, at a specific version, with specific constraints, is knowledge that exists in engineers’ heads, in code, and in previous product decisions. An AI assistant has access to none of that unless you provide it explicitly. Without that grounding, it generates documentation that sounds right but isn’t.
The most careful thinking I’ve read on what this means for the profession is at idratherbewriting.com. Tom Johnson’s “cyborg technical writer” framing is useful: the writer’s job shifts from raw drafting toward editing, verification, and structural judgment. The AI handles the first pass; the writer handles the part that requires knowing what’s actually correct.
At passo.uno, Fabrizio Ferri-Benedetti examines how AI changes documentation as a practice rather than just a task — less focused on the tools themselves, more on what shifts in the actual work.
The practical takeaway is modest and correct: AI tools can make parts of a technical writer’s work faster. They can’t make it unnecessary. The value a technical writer provides comes from understanding the product, understanding the user, and making judgments about what needs to be said and how. Those judgments are not what the AI is doing when it generates a parameter description.
Verify the output. Every time.