AI Code Documentation: READMEs, Docstrings and Comments

AI code documentation is one of the safest, highest-value uses of an assistant in software work. Given your code, an assistant can draft docstrings, READMEs, API references and change notes in minutes, the tasks developers most often postpone. The catch is that documentation is only useful if it is true, and an assistant describes what the code appears to do, not what you intended. So the workflow is: generate from the real code, review against behaviour, and keep it close to the code so it stays current.

This guide covers which kinds of documentation AI writes well, prompts for each, a review checklist, and how to stop generated docs from rotting.

Why documentation is a good fit for AI

Most documentation is a translation job: from code, which machines read easily, into prose and examples, which people read easily. Language models are strong at exactly that kind of translation. They are also tireless about the boring parts, such as listing every parameter, every return type and every exception, which humans tend to skip.

Documentation is also lower risk than generated code. A wrong docstring is bad, but it does not crash production, and a reviewer can check it by reading. That makes it a sensible place for teams to start using AI in their workflow.

What it does not replace

AI cannot know the things that are not in the code: why a design decision was made, which approach was tried and abandoned, what the business rule is behind a strange condition, or which part of the API is about to be deprecated. Those are often the most valuable parts of documentation, and they still need a human.

The four kinds of documentation, and how well AI handles each

A useful way to think about documentation is the Diátaxis framework, which separates four kinds of writing with different purposes. AI helps with each to a different degree.

Type	Purpose	How well AI drafts it	What you must add
Reference (docstrings, API docs)	Accurate description of each function, class, endpoint	Very well	Intent, edge-case guarantees, deprecations
How-to guides	Steps to achieve a specific goal	Well, if given the real setup	Real commands, tested steps
Tutorials	Learning by doing, for newcomers	Moderately	A tested path that actually works end to end
Explanation (architecture, decisions)	Why things are the way they are	Poorly without input	The reasons, history and trade-offs

The pattern is clear: the closer the documentation is to the code itself, the better AI does. The further it moves toward intent and history, the more it depends on what you tell it.

Docstrings and inline comments

This is where AI code documentation saves the most time. Paste a function or a whole module and ask for docstrings in your project’s convention.

Prompt template

“Add docstrings to every public function and class in this Python module. Follow PEP 257 and the Google docstring style. For each function, describe what it does in one line, then Args, Returns and Raises. Do not change any code. Do not describe behaviour you cannot see in the code; if something is unclear, add a TODO question instead.”

The final instruction is important. It turns uncertainty into a visible question rather than a confident guess. The conventions themselves are defined in PEP 257 for Python; for JavaScript, JSDoc is the common standard, and most languages have an equivalent.

Comments: fewer and better

A common mistake is asking AI to “comment this code”, which produces a comment on every line restating what the code says: “increment i by one”. That adds noise. Ask instead for comments only where the reason is not obvious:

“Add comments only where a reader would ask ‘why?’: non-obvious conditions, workarounds, performance choices, business rules. Do not comment on what the code obviously does.”

Because the assistant does not know the reasons, it will often propose a guess. Treat each such comment as a question for whoever knows the answer.

READMEs that people actually read

A README is the front door of a project. A good one answers, in this order: what it is, why someone would use it, how to install it, how to use it in the simplest case, and where to go next.

What to give the assistant

• The project’s main entry points, configuration file and package manifest.
• The actual install and run commands you use.
• A one-sentence description of who it is for.
• Any existing README, even if out of date.

Prompt template

“Write a README for this project. Audience: developers integrating it into a web shop. Sections: one-paragraph overview, requirements, installation, configuration (list every environment variable from the config file with its default), quick start example, running tests, licence. Use only commands and variables that appear in the files I gave you.”

Then test it: follow the README on a clean machine or container, exactly as written. Every step that fails is a documentation bug, and you will be surprised how many there are the first time.

API docs, changelogs and release notes

API documentation from code

For HTTP APIs, an assistant can read route handlers and data models and draft endpoint documentation: method, path, parameters, request body, responses and errors. It can also draft an OpenAPI description, which many tools can then render as interactive docs.

Two cautions:

1. Error responses are often incomplete. Assistants document the errors they can see in the handler, but miss ones raised by middleware, authentication layers or the framework. Check against real responses.
2. Examples must be real. Ask for example requests and responses, then run them against a test environment and paste in the real output. Fabricated example JSON that does not match reality is worse than no example.

Changelogs, release notes and commit messages

These are small but frequent writing tasks where AI saves time every week.

• Commit messages: paste a diff and ask for a conventional commit message with a short summary and a body explaining why. Edit the “why”, because the diff only shows what.
• Changelogs: paste the list of merged changes and ask for a grouped changelog (Added, Changed, Fixed, Removed) written for users, not developers.
• Release notes: ask for a version aimed at customers: what is new for them and what they need to do, if anything. Leave out internal refactors.

Pull request descriptions fit here too. An assistant can summarise a diff for reviewers, which pairs well with the process in our guide on code review with AI before you merge.

Documenting legacy code you did not write

AI is particularly valuable when you inherit an undocumented codebase. A practical sequence:

1. Map first. Ask for a summary of each module’s responsibility and how the modules depend on each other.
2. Trace key flows. “Walk through what happens when an order is placed, from the controller to the database, naming each function.”
3. Record unknowns. Ask the assistant to list everything that looks surprising or unexplained. That list becomes your questions for colleagues.
4. Write docs as you confirm. Only commit documentation for behaviour you have verified by reading, running or testing.

This fits naturally before any refactoring; our guide to refactoring legacy code with AI starts from the same understanding step.

Reviewing AI code documentation before you merge it

Generated documentation should go through review like code. Use this checklist:

• Does every claim match the code? Parameter names, types, defaults, return values and raised exceptions.
• Are guarantees real? Words like “always”, “never”, “thread-safe” or “idempotent” must be true, not assumed.
• Are examples runnable? Copy each one and run it.
• Is anything invented? Configuration options, flags or endpoints that do not exist are a classic failure.
• Is the reason recorded where it matters? Add the “why” for non-obvious decisions yourself.
• Is it the right length? Trim restatements of the obvious. Short, accurate docs get read; long ones get skipped.

Privacy: check what code you share

Before pasting code into any assistant, check your company’s rules. Remove secrets such as API keys, passwords and connection strings; they do not belong in documentation anyway. Prefer tools that do not use your input for training. For client work, confirm that sharing code with an external service is allowed under your contract.

Keeping AI code documentation from going stale

The biggest problem with documentation is not writing it but keeping it true. AI makes regeneration cheap, which helps, but only with some discipline.

• Keep docs next to code. Docstrings and a README in the repository are updated in the same pull request as the code, and reviewers see both.
• Make “update the docs” part of the diff prompt. When you ask an assistant to change a function, also ask it to update the docstring and any README section that mentions it.
• Test your examples. Many languages can run examples in docstrings as tests. If an example breaks, the build fails, and the docs get fixed.
• Periodically diff docs against code. Paste a module and its documentation and ask: “List every statement in the docs that no longer matches the code.”

How Ask Mio helps with AI code documentation

Ask Mio’s Code mode is built for this kind of work. It routes coding requests to models suited to code, and you can upload source files or whole sets of files to document them together. Code mode is part of the Coding plan (12 € a month) and the Business plan, which also include a Python sandbox for running examples and connectors with write actions. The GitHub, Notion and other developer connectors let Mio read a repository when you ask it to.

Projects are useful here: put your documentation conventions, such as docstring style, README template and changelog format, in a project’s instructions once, and every chat follows them.

Where others are better: tools built into your code editor see the whole workspace continuously and can update docs inline as you type. An assistant is better for larger documentation jobs, explanations and reviews that benefit from a conversation.

Frequently Asked Questions

Can AI write good code documentation?

Yes, especially reference documentation such as docstrings, parameter lists and API endpoint descriptions, which follow directly from the code. It is weaker at explaining why decisions were made, because that information is not in the code. Review every generated statement against the actual behaviour and add the reasons yourself.

How do I stop AI from inventing details in documentation?

Tell it explicitly to describe only behaviour visible in the code you provided, and to add a TODO question wherever something is unclear. Give it the real configuration files and commands. Then review with a checklist, and run every example, because invented options and fabricated sample output are the most common errors.

Should I ask AI to comment every line of code?

No. Line-by-line comments that restate the code add noise and go stale quickly. Ask for comments only where a reader would wonder why: unusual conditions, workarounds, performance choices and business rules. Good naming and docstrings usually explain the what; comments should explain the reasons.

Can AI generate a README from my repository?

It can draft a solid README if you give it the entry points, manifest, configuration and the commands you really use. Ask it to use only commands and variables found in those files. Then follow the README on a clean setup exactly as written and fix every step that does not work.

Is it safe to paste company code into an AI assistant?

It depends on your company’s policy and the tool’s data handling. Remove secrets first, check whether your input is used for training, and confirm where data is stored. Ask Mio does not use chats or files for training and stores data in the EU, but your employer’s rules still come first.

Which Ask Mio plan do I need for code documentation?

You can ask for docstrings or a README in Chat mode on any plan, including the free one. Code mode, with coding-focused model routing, larger file uploads and a Python sandbox for running examples, comes with the Coding plan at 12 € a month and the Business plan.

The Bottom Line

AI code documentation removes most of the drudgery from docstrings, READMEs, API references and changelogs, and it does it well because those texts follow directly from the code. Your job moves from writing to verifying: check every claim, run every example, and add the reasons only you know. Keep docs next to the code and regenerate them with each change so they stay true. Try it with Mio’s Code mode on the Coding plan, or compare all options on the Ask Mio pricing page.