AI API Documentation: Endpoints, Examples, OpenAPI

AI API documentation is one of the best uses of an assistant for developers, because the raw material already exists: route handlers, request examples, error codes and a few lines of comments. An assistant can turn that into an OpenAPI description, readable endpoint pages and working examples in several languages in minutes. What it cannot do is know what your API actually does when it differs from what the code seems to say, so every output needs a check against reality.

This guide shows a workflow for documenting a REST API with an assistant such as Ask Mio: what to paste in, what to ask for, how to produce an OpenAPI file, how to write examples that run, and the specific mistakes to look for before you publish anything.

What good API documentation contains

Before asking an assistant for anything, decide what the finished documentation should include. Most readers of API documentation want the same few things and leave quickly when they cannot find them.

A quick start. The shortest path from “no account” to “first successful call”, ideally under ten lines.
Authentication. How to get a key or token, where to send it and what a missing or wrong one returns.
A reference for every endpoint. Method, path, parameters, request body, response body and every status code the endpoint can return.
Examples. A request and a real response for each endpoint, in the languages your users use.
Errors, limits and versioning. What the error format looks like, what the rate limits are and how breaking changes are announced.

An assistant is good at the structured parts of that list and at turning technical facts into plain sentences. It is weak at facts it cannot see: rate limits, undocumented behaviour and business rules. Plan to supply those yourself.

AI API documentation: what to give the assistant

The quality of the result depends almost entirely on what you paste in. Vague input produces vague documentation, and an assistant fills gaps with plausible guesses. Give it real material.

Source material, in order of usefulness

The route definitions and handlers. Paste the code for one resource at a time, not the whole repository.
Validation schemas or types. These tell the assistant which fields are required, which are optional and which values are allowed.
Real request and response samples. Capture them from a working call with secrets removed.
A list of error responses. Include the status code, the body and what causes each one.
Your own notes. Rate limits, pagination rules, idempotency, anything that is true but not visible in the code.

What to leave out

Remove API keys, tokens, customer data and internal hostnames before pasting. Replace them with obvious placeholders such as YOUR_API_KEY. Our guide on what not to share with AI lists the categories to check.

Step by step: from handler to endpoint page

Step 1: ask for a structured summary first

Do not ask for finished documentation straight away. Paste one handler and ask: “List the method, path, path and query parameters, body fields with types and whether they are required, the success response and every error response you can see in this code. Mark anything you are inferring rather than reading.” The last sentence matters. It turns silent guesses into visible ones that you can confirm or correct.

Step 2: confirm the inferred items

Go through the items marked as inferred and fix them. This is the step that stops invented behaviour from reaching your readers. It usually takes a few minutes per endpoint and removes most factual errors.

Step 3: write the human description

Now ask for a two-sentence description of what the endpoint does, written for a developer who has never seen the system, followed by notes on common mistakes. Keep the style consistent by pasting one approved endpoint page as a model and saying “match this structure and tone”.

Step 4: generate the examples

Ask for a curl request and a response body that match the summary exactly. Then ask for the same call in the languages your users use. Examples should be copy-and-run, so keep placeholders obvious and avoid made-up values that look real.

curl -X POST https://api.example.com/v1/invoices \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"customer_id": "cus_123", "amount": 4200, "currency": "EUR"}'

Producing an OpenAPI specification

An OpenAPI description is a machine-readable version of your reference. Once you have one, many tools can render pages, generate client libraries and run mock servers from it. An assistant can write the first version from the structured summaries you confirmed in the steps above.

How to ask for it

Say which version of the specification you target, paste the confirmed summaries for a group of related endpoints, and ask for a YAML file with reusable schemas under components, one entry per resource. Ask it to include example values, required fields and every documented response code. Keep each request to one resource group, because very long outputs are more likely to drift.

How to validate it

Never publish a specification that has not been through a validator. Open it in an OpenAPI linter or editor, fix the reported errors and then compare the rendered pages with the real behaviour of two or three endpoints. The OpenAPI Initiative site lists the tools that exist. If a validation error persists, paste the exact message back to the assistant with the faulty section; this is the same loop described in our guide to debugging from an error message.

Where assistants go wrong in API documentation

These failures are common enough to check for every time.

Failure	What it looks like	How to catch it
Invented parameters	A filter or field that sounds sensible but does not exist	Compare each parameter with the code or a real call
Wrong status codes	200 documented where the API returns 201, or 400 where it returns 422	Run each example and read the actual status line
Optional shown as required	A field marked mandatory because it appears in every example	Check the validation schema, not the examples
Missing error cases	Only the happy path is documented	Ask for every branch in the handler that returns an error
Stale examples	Field names from an earlier version	Regenerate examples from current samples after each release
Plausible but false limits	A rate limit or maximum size stated with confidence	Only state limits you supplied; delete any the assistant added

The pattern behind most of these is the same: the assistant writes what is likely, not what is true. That is why the confirm step matters and why our article on when not to trust AI-generated code applies equally to documentation.

Writing the parts readers actually use

The quick start

This is the page most readers see first and the one most worth writing by hand, with the assistant as an editor. Draft the steps yourself, then ask it to find places where a newcomer could get stuck: an unexplained term, a missing prerequisite, a step that assumes an earlier one succeeded.

Error reference

List every error code once, in a single table, with the cause and the fix. Ask the assistant to group them by who must act: the caller, the caller’s account owner or the API team. Readers then know whether to change their request or to contact support.

Changelogs and migration notes

When the API changes, paste the old and new specifications and ask for a list of breaking changes, additions and deprecations. Then edit it for tone and add the migration steps yourself. The same review habit applies as in our guide to code review with AI before you merge.

Keeping documentation current

Documentation dies when it lives apart from the code. Three habits keep it alive. First, treat the specification as part of the pull request: a change to an endpoint changes its description in the same review. Second, regenerate examples from real calls after each release rather than editing them by hand. Third, keep a short checklist of the facts only you know, such as limits and deprecation dates, so the assistant is always given them and never has to guess.

The same approach works for internal APIs. For teams, a shared project can hold the style guide, an approved endpoint page and the glossary of terms, so every session starts with the same context. Our overview of projects and memory explains how that works in Ask Mio.

A worked example: a small invoices API

Suppose an API has five endpoints for invoices: create, get, list, update status and delete. Here is how a developer might document it in one afternoon.

Paste the create handler and its validation schema. Ask for the structured summary with inferred items marked. Correct two inferred defaults.
Repeat for the other four endpoints. Total time: about twenty minutes.
Paste all five confirmed summaries and ask for an OpenAPI file with a shared invoice schema. Run it through a validator and fix two errors with the assistant.
Run each example call against a test environment. Two return 201, not 200; correct the specification and the pages.
Ask for a quick start that creates an invoice and retrieves it, and for an error table. Edit both for tone.
Add the rate limits and pagination rules from your own notes. Ask the assistant to check that every page mentions them where relevant.

The result is a reference that is faster to write than from scratch and that has been checked against the real service. The assistant did the repetitive structuring; you supplied the facts and the verification.

Which Ask Mio plan fits this job

Documentation work is mostly Chat and Write mode, with Code mode useful when you want the assistant to produce and test client examples. The Coding plan at €12 a month includes Code mode and a code execution sandbox, with 6,000 points a month. The Chat plan at €5 a month, with 2,000 points and file uploads, is enough for occasional documentation, and the free plan gives 600 points for trying the workflow. Business at €29 a month includes 5 seats and API access. A coding answer costs 5 to 10 points, a normal chat reply 1 point. These figures come from the live pricing page.

Frequently Asked Questions

Can AI write API documentation from code alone?

It can produce a useful first draft from handlers and schemas, but not a finished reference. It will describe what the code appears to do, and it may guess at behaviour the code does not show. Ask it to mark inferred items, confirm them against a running service, and add limits and business rules yourself.

How do I get an OpenAPI file from an assistant?

Paste confirmed endpoint summaries for one resource group, name the specification version and ask for YAML with reusable schemas, examples and every response code. Then run the result through a validator and fix errors by pasting the exact messages back. Keep requests small, because long outputs drift more.

Are AI-written code examples safe to publish?

Only after you run them. Examples often look right and fail on a small detail such as a header name, a status code or a field type. Test each one against a real or test environment, remove any real credentials and keep placeholders obvious so readers cannot confuse them with working values.

What should I never paste into the assistant?

Never paste live API keys, tokens, customer records, private hostnames or anything under a confidentiality obligation. Replace them with placeholders before you paste. Anonymise sample payloads too, because real names and email addresses often hide in example responses captured from production.

How often should documentation be regenerated?

Update it with every change that touches an endpoint, as part of the same review. Regenerate examples from fresh calls after each release, and run a full check against the real service before major versions. Documentation that is edited only when someone complains drifts away from the product quickly.

Does the assistant replace a technical writer?

No. It speeds up structure, first drafts and examples, which are the repetitive parts. A writer still decides what readers need, keeps the voice consistent across pages and notices what is missing. Teams without a writer can reach a solid baseline with an assistant, if they verify every claim.

The Bottom Line

Use an assistant to do the repetitive structuring of API documentation, and keep the facts and the verification for yourself. Give it real code and samples, make it mark what it inferred, validate the OpenAPI file and run every example before publishing. That workflow turns a week of tedious writing into an afternoon without trading away accuracy. Try it on one endpoint with the free plan, then compare the Ask Mio plans if you want Code mode and the sandbox.

AI API Documentation: Endpoints, Examples and OpenAPI Specs