Automatic documentation can turn a maintained application definition into a consistent reference. It works best when the same definition also controls the behavior being described. Keep generation reproducible, test consequential changes and give people the task explanations that a list of fields cannot supply.
Choose an authoritative definition
Start with the part of the application that already has a structured description: input fields, commands, database tables or an API contract. Identify its owner, supported versions and consumers. A generator can faithfully repeat an outdated description, so decide how changes to the running application reach this source.
The OpenAPI specification is one established approach to describing HTTP APIs. The downloadable example below deliberately uses a much smaller custom application definition. It is neither an OpenAPI document nor a general JSON Schema implementation.
For a production project, choose a specification and generator version supported by the actual toolchain. Record the dependency versions and generation command with the release. Treat generated files as reproducible outputs rather than inviting separate edits that will disappear on the next build.
Run a small shared-definition example
Download the application documentation example. It contains a fictional equipment-request definition, a validator, a documentation builder, sample input, tests and a prebuilt HTML reference. It runs with Python 3.10 or later using only the standard library. Python's JSON documentation describes the parsing and serialization used by the sample.
| Field | Application rule | Reference explanation |
|---|---|---|
| item | Required nonempty text | Name the requested equipment. |
| quantity | Required integer from 1 through 5 | How many units are requested. |
| note | Optional text | Context for the service desk. |
Extract the ZIP into a test folder, read README.txt, then run python build.py and python tests.py. Open reference.html to inspect the generated table, or preview the supplied reference. Run python app.py to validate the included request. The scripts read local sample files and write the reference output; they make no network calls.
Make a change that tests the connection
A changed limit can affect existing integrations and instructions. Record the reason, identify consumers, revise examples and choose a release process that tells affected people what changed. Merely regenerating a document does not coordinate the rollout.
Write the explanations people still need
A field reference can tell a reader that quantity is an integer between one and five. A task guide explains how to choose equipment, who may request it, what happens after submission and whom to contact when the request is declined. These require service knowledge and accountable review.
Keep examples executable where practical. Use representative failures as well as successful requests, and explain the recovery step. Mark private or internal material appropriately; a generator should publish only the intended audience's fields, examples and descriptions.
If an AI assistant drafts explanatory prose, review its claims against the application's supported behavior and approved policy. Generated confidence or fluent wording supplies no evidence that an undocumented permission, limit or feature exists.
Check documentation as part of each release
- Review the changed definition and identify affected users and tasks.
- Generate the reference using the recorded toolchain.
- Run application and documentation tests on the same revision.
- Inspect the rendered reference, examples, links and audience restrictions.
- Publish the matching application and documentation versions through the agreed release process.
- Record feedback and assign an owner to unresolved documentation gaps.
The sample demonstrates a shared source and a few meaningful tests. It is an educational validator, not production request-handling or security software. The included maintenance checklist helps transfer the pattern to an appropriate toolchain.