How to Use AI to Write Technical Documentation Your Team Will Actually Read?

Most technical documentation is tricky. If not handled skillfully, it can take too long to write, get outdated before it gets shipped, and end up either too vague for developers or too dense for everyone else. Teams know the documentation needs to be better, but nobody has the time to actually fix it.

AI makes the problem manageable by handling the parts that kill momentum:

• Drafting from scattered notes and raw inputs
• Updating sections whenever the product changes
• Structuring content so it works for more than one audience

That frees writers and developers to focus on what actually needs a human eye, like checking accuracy and making sure the documents reflect how users think about the product.

This guide walks through how to use AI at every stage of technical documentation, from gathering context all the way to keeping content current.

What Is Technical Writing?

Technical writing is the process of turning complex information into clear, accurate documentation for a specific audience. It covers API references, setup guides, runbooks, release notes, changelogs, and onboarding docs.

What makes it hard is not the writing itself. The challenge comes from multiple simultaneous demands, including:

• Deep product knowledge
• Clear understanding of the target audience
• Maintaining consistent structure across documents
• Keeping documentation continuously updated as the product evolves

Most people writing technical documentation are also doing five other things, which is why it falls behind. Good technical documentation is specific, written for the reader rather than the author, easy to navigate, and accurate enough to trust.

Step-by-Step Guide To Technical Documentation With AI

Once your source material and structure are ready, AI follows a clear step-by-step process to create the draft. The diagram below shows what goes in and what comes out at each stage, so you know where to focus and what AI handles for you.

Let’s dive deeper into each step and see how it works in practice.

Step 1: Define Scope and Audience Before Writing Anything

Most technical documentation fails not because it is poorly written but because nobody agreed on what it was supposed to cover or who it was for before writing started. The result is documentation that is too broad, too narrow, or written for the wrong reader entirely.

Before writing anything, three questions need clear answers:

• Who is reading this?
• What should they be able to do after reading it?
• What does this document not cover?

A setup guide for a first-time developer looks completely different from a reference doc for an experienced one. Getting this wrong wastes the draft entirely.

Step 2: Research and Information Gathering

Most documentation stalls before it starts because the right information is scattered everywhere. A developer documenting a new feature might need to pull context from a Slack thread, a Jira ticket, internal notes, and code comments before writing a single sentence. discuss MCPS of different tools, etc., like Claude, etc

Before AI can do anything useful, someone needs to supply the accurate raw material. That usually means talking to the people who built the product. Subject matter experts (SMEs), engineers, product managers, or support leads hold the context that never makes it into tickets or commit messages: why a decision was made, what edge cases exist, and how users are likely to misuse a feature.

Clear documentation with AI assistance starts with strong subject matter input. Before drafting, it’s important to:

• Before drafting begins, schedule a short interview or asynchronous walkthrough with the relevant SME (Subject Matter Expert).
• Record the session, take detailed notes, or request that the SME complete a structured brief.
• Compile the SME input and combine it with any other supporting sources or reference material.
• Provide this consolidated input to the AI as context for drafting.
• The quality of the AI-generated output is directly dependent on the quality of the input provided.
• SME contributions are typically the most reliable and valuable source within the information stack.

Chatly's AI search engine is useful here for researching topics you need to understand before writing, whether that is how a specific API pattern works, what a framework convention means, or what a technical term should actually say in plain language.

For compressing long internal threads or dense docs into a workable brief, the summary generator does this quickly without you having to read everything line by line.

Step 3: Planning Structure and Audience

Before writing anything, two questions need clear answers: who is reading this, and what do they need to be able to do after reading it? A setup guide for a first-time developer looks completely different from a reference doc for an experienced one. Getting this wrong wastes the draft entirely.

Chatly's AI chat is well-suited for this planning stage because you can continue the same conversation you started in Step 1. The context you have already established, the SME notes, the source material, and the scope of the feature carry forward.

You do not need to re-explain the product to get a useful outline. Ask it to propose a structure for a specific audience, and it builds the outline from what it already knows about the feature, not from generic assumptions.

Prompt:

You are a senior technical documentation strategist. I am writing documentation for a [feature name]. The target audience is [first-time developers / experienced developers / API consumers]. Based on the following context (SME notes, feature description, and any reference material), create a structured documentation outline. Ensure the structure is tailored specifically to this audience’s level of expertise and real-world usage needs. Focus on clarity, logical flow, and practical usability rather than generic sections.

Before committing to a structure, it also helps to check what similar documentation looks like. The Chat PDF feature lets you upload existing API docs, competitor documentation, or internal reference material and ask questions about how it is structured, what it covers, and what it might be missing.

Step 4: Writing the First Draft

Once you have a clear structure and source material, AI can produce a first draft that covers the right sections in the right format. Your job then becomes editing and verifying rather than writing from scratch.

This works well for documentation types that have a consistent format but different content each time:

• API reference docs built from a spec or code comments
• Installation and setup guides from engineering notes
• Release notes and changelogs from commit messages or sprint notes
• Onboarding guides and configuration walkthroughs

The AI document generator is designed specifically for this workflow, producing structured, formatted documents instead of raw chat responses that require manual reformatting. It keeps content cleanly organised into clear sections, making it ready to use with minimal editing and reducing the need to restructure or polish the output afterward.

For documentation tied directly to code, the AI coder app lets you feed in actual function names, code comments, and source files alongside your documentation request so the output reflects what the code actually does.

Prompts that produce useful drafts

The difference between a generic output and a usable draft comes down to specificity. Always tell the AI the format, the audience, and the source material.

For API documentation:

"Write API documentation for this endpoint based on the spec below. Include a one-sentence description, required and optional parameters with data types, a sample request and response, common errors with explanations, and one practical use case."

For a setup guide:

"Write an installation guide for [product] for developers setting it up for the first time on macOS. Include prerequisites, exact steps with commands, how to verify it worked, and three common errors with fixes."

For release notes:

"Write release notes for version 2.4 based on the notes below. Include a one-sentence summary, new features with brief descriptions, bug fixes, and breaking changes with migration steps."

For a troubleshooting section:

"Based on the support data below, write a troubleshooting section for the five most common problems. For each one, explain the cause and give a fix in under 50 words."

For building reusable prompt templates across documentation types, the system prompts guide covers how to structure them for consistent results.

Step 5: Creating Visuals and Diagrams

Text alone rarely makes technical documentation clear enough. Architecture diagrams, workflow charts, annotated UI screenshots, and concept illustrations do the work that paragraphs cannot, especially for setup sequences, system relationships, and multi-step processes where a reader needs to see how the pieces fit together.

AI handles a significant part of this. For concept illustrations, workflow diagrams, and explainer graphics, Chatly's AI image generator produces visuals directly from a written description.

Describe the diagram you need, e.g., a data flow between two services, a deployment architecture, a user journey through an onboarding sequence, and use the output as a working visual that can be refined or dropped straight into the document.

Step 6: Reviewing and Editing for Accuracy

The draft is a starting point. Every procedural step needs to be tested against the actual product, every code example needs to run, and every label or setting name needs to match what is visible in the interface. AI writes from what it is given and cannot test what it produces.

Beyond accuracy, AI is useful for the editing pass itself. It catches awkward phrasing, overly long sentences, and places where the writing assumes too much from the reader.

Claude Sonnet 4.6 is particularly good at this because it follows specific rewriting instructions carefully and keeps terminology consistent across a long document without losing technical precision.

Common edits that make the biggest usability difference:

• Breaking long feature descriptions into numbered steps
• Adding a short expected outcome at the end of each step
• Switching passive constructions to direct instructions
• Pulling a quick-start section out of a longer reference document

Be specific in your editing prompt. "Rewrite this for a developer who has never used this API" gets a better result than "make this clearer."

Step 7: Consistency Across the Documentation Set

When multiple people write documentation over time, terminology drifts. One writer uses "workspace," another uses "project," a third uses "environment." Each is fine alone, but together they make documentation feel unreliable, which is a serious problem for developer audiences where precise language matters.

AI can scan a full documentation set and flag every place where a preferred term has not been used, where heading styles differ, where code blocks are formatted inconsistently, or where the writing breaks style guide rules. What it returns is a list of issues to fix, not automatic changes, because context still requires human judgment.

Paste your style guide rules directly into the prompt and ask AI to identify violations. It catches passive voice, inconsistent capitalisation of product names, and mixed use of second and third person reliably across long documents.

Step 8: Troubleshooting Sections and FAQs

Troubleshooting content and FAQs are what users read first when something goes wrong, and they are the hardest to write well because the most useful answers come from real support data, not from the writer's assumptions about what users will ask.

AI can read through a batch of support tickets, identify the most common problems, and structure each one as a clear problem-cause-fix entry. Documentation built this way answers the questions users are actually asking rather than the ones the writer anticipated.

For developer-facing troubleshooting, the code AI feature goes further by analysing both the error message and the code that produced it. That combined context produces troubleshooting entries that are specific to the actual failure rather than a generic answer dressed up as a fix.

Step 9: Updating Documentation When the Product Changes

Documentation falls out of date almost immediately after it is published. A label changes, an endpoint gets renamed, or a setup step no longer matches the interface. Most teams only discover this when a new team member gets stuck, or a support ticket arrives.

AI makes updates fast enough to handle in the same pull request as the code change. You paste the affected documentation section alongside a note describing what changed, ask AI to update only that part, and it also flags other places in the document that reference the same element, so nothing gets missed.

For documentation that needs to work for both technical and non-technical readers, such as internal specs, release summaries, or handover documents, the business reports section of the document generator covers how to structure writing that serves both audiences without becoming two separate documents.

Common Mistakes in AI-Assisted Technical Documentation

AI speeds up documentation significantly, but the same patterns tend to produce poor results across teams. These are the ones worth watching for.

Publishing Without Accuracy Checks

AI writes from what it is given and cannot test what it produces. Every procedural step needs to be followed as written, every code example needs to run, and every label needs to match what is visible in the actual product before the document goes out.

Giving AI too Little Context

A prompt like "write API documentation for this endpoint" produces generic output. Specifying the audience, the format, the expected inputs and outputs, and any edge cases the reader needs to know about produces something usable. The quality of the output is directly limited by the quality of what goes in.

Writing For the Wrong Audience

Documentation written for an experienced engineer will lose a first-time user at the first unexplained term. Defining the audience clearly before drafting, as covered in Step 1, prevents this from happening after the document is already written.

Never Updating Documentation After it Ships

Documentation that was accurate on day one becomes a liability the moment the product changes and the docs do not. Pasting the affected section into Chatly's AI Chat alongside a description of what changed takes a few minutes and is fast enough to do in the same pull request as the code change.

Treating the First Draft as Finished

AI produces a strong starting point, not a final result. The first draft establishes structure and covers the right ground. The editing pass is where clarity, accuracy, and usability actually get built in.

Start Writing Better Technical Documentation Today

Good technical documentation does not have to take forever to write or fall apart every time the product changes. With a clear process and AI handling the parts that kill momentum, teams can produce documentation that is accurate, readable, and stays current.

The best way to see how it works is to try it on something real. Pick a document your team has been meaning to fix, gather the source material, and let AI do the heavy lifting on the first draft.