AI-Generated Documentation: Don’t Fear the Reaper

When I first saw that Claude could read source code and generate documentation from it, I was terrified.

Not mildly concerned. More like wondering whether Costco was hiring.

I’d seen an application built largely through prompts, with context-sensitive help generated as part of the development process. The documentation wasn’t a separate project waiting for someone to tackle after the software was finished. It arrived alongside the application.

For someone who has spent more than twenty years writing technical documentation, that gets your attention.

It should. If a machine can produce in minutes what once occupied a writer for days, pretending nothing has changed isn’t a strategy.

But neither is assuming that the first draft represents the entire job.

The Uncomfortable Part Is Real

Let’s get something out of the way: AI could allow fewer writers to produce more documentation.

One experienced writer using AI may be able to handle work that previously required several people. Some companies will reduce staffing. Others will hire a contractor for work they once assumed required a permanent position. Some will assign documentation to engineering and decide that the generated output is good enough.

There’s no useful argument for pretending those possibilities don’t exist.

The continued need for writing skills doesn’t guarantee the same number of writing jobs. Nor does it guarantee that every responsibility will remain inside a department called Technical Publications.

That’s an uncomfortable distinction, but it’s essential. We can make a credible case for the value of technical writers without promising that our profession is immune to changes affecting everyone else.

The question is what companies still need when producing sentences becomes easier.

The Code Knows What Was Built

Generating documentation from source code is a significant capability.

The implementation can reveal available options, accepted values, dependencies, and application behaviour. For API reference material and other content closely tied to the code, that can provide a valuable starting point.

But the implementation isn’t the whole explanation.

Consider an application with a setting labelled Connection timeout. An AI system with access to the code might correctly identify the default value, permitted range, and what happens when the timeout expires.

Those are useful facts.

A customer may have a different set of questions:

• Why would I change this?
• What problem would increasing it solve?
• What are the consequences of setting it too high?
• Should I investigate something else first?

The implementation may contain clues. The answers may also depend on deployment conditions, support experience, product decisions, and knowledge that was never recorded in the repository.

This is an illustrative example, but it shows the distinction. Describing a setting and helping someone make a sound decision about it are related tasks. They aren’t identical.

A writer has to recognize when the description stops short of the help the reader needs.

Documentation Has to Pass a User Test

Technical writing isn’t entirely subjective. An instruction can name the wrong control, omit a prerequisite, or describe a feature that isn’t available in the supported release. Those are defects.

Other failures are harder to detect by reading the text alone.

A procedure can be technically accurate and still assume knowledge the intended audience doesn’t have. It can explain every individual action while leaving the reader unsure why they’re performing them. It can describe the successful path beautifully and offer nothing when the expected result doesn’t appear.

The test isn’t simply whether the paragraph sounds professional.

Can the intended reader use it to complete the task?

Answering that requires more than proofreading. It means understanding the product, identifying assumptions, performing procedures, asking questions, and deciding which gaps matter.

AI can assist with those activities too. The writer’s contribution is knowing what needs to be established and what evidence is sufficient to approve the result.

That’s a more demanding responsibility than making generated prose sound human.

More Pages Aren’t Necessarily More Help

When drafting is expensive, the cost of producing a page creates a natural limit on how much documentation gets written.

AI loosens that constraint.

A team can generate an overview, a quick-start guide, a detailed guide, a troubleshooting page, and several explanations of the same feature with relatively little effort.

Some of that content may be useful. Some may repeat material that already exists. Some may introduce a second explanation that subtly contradicts the first.

Every additional page also becomes something that can go out of date.

The resulting problem isn’t a shortage of words. It’s deciding which words deserve to remain.

An experienced writer may create value by consolidating five generated pages into one useful procedure. By removing an unnecessary explanation. By recognizing that a screenshot and three clear steps would serve the reader better than another thousand words.

Writing skill includes knowing what to leave out.

That judgment becomes more important when generating additional content is almost effortless.

The Service Changes Along With the Work

This has practical implications for companies that need documentation but don’t have enough ongoing work to justify a full-time writer.

They may already have generated drafts. Their engineers may have produced API references. Their application may already contain embedded help.

Hiring a technical writer doesn’t have to mean throwing that work away and starting again.

The engagement can begin with what exists:

• Assess the generated material against the intended audience and release.
• Identify missing tasks, unsupported claims, and unnecessary repetition.
• Verify procedures against the product.
• Resolve questions with the appropriate subject matter experts.
• Deliver a coherent documentation set that’s ready to use.

The work should follow the company’s approved AI policies and use the tools appropriate to the assignment. Where AI helps, use it. Where direct investigation, testing, or careful rewriting is needed, do that.

The value of the service is the finished result, not the amount of manual typing involved.

A smaller team with better tools can be a sensible arrangement. So can an experienced contract writer brought in for a release, an overhaul, or a defined documentation problem.

Neither arrangement eliminates the need for expertise. It changes how the company obtains it.

Writers Need to Make the Case Through the Work

There’s a temptation to defend technical writing by insisting that AI will never match human writing.

I wouldn’t build a career or a business around that promise.

The tools will improve. Some weaknesses we identify today will become less significant. Tasks that currently require close attention may become easier to automate.

Our argument has to be more specific than “a human should check this.”

What needs checking? Against which source? For which audience? What could go wrong? What would make the content ready to publish?

A writer who can answer those questions offers something concrete. A writer who can show how their intervention prevented a misleading instruction, simplified a difficult workflow, or removed contradictory guidance makes the value visible.

That’s a stronger position than asking a company to preserve a familiar job description.

Don’t Confuse a Changing Role With an Unchanged Market

I don’t think the sensible response to AI-generated documentation is denial. I also don’t think every impressive demonstration is a reason to abandon the profession.

The work is changing. The staffing model may change with it.

Experienced technical writers already investigate products, challenge assumptions, test instructions, and shape information around users’ needs. AI can shift more of their time toward those responsibilities by reducing the effort spent on initial production.

That won’t protect every job. It does provide a credible direction for the service we offer.

Companies may need fewer people producing first drafts. They still need documentation that helps customers use their products.

My job is to help close the distance between those two things.