How to Write Documentation for Code with AI: A Reviewable Workflow

To write useful documentation for code with AI, start with clear code, provide the assistant with source from a known revision and relevant tests, and treat its response as a draft. The responsible engineer must verify behavior and supply or confirm rationale before anything is published.

Useful documentation helps readers use code correctly, understand important behavior, and find its rationale or operating instructions. A docstring serves someone calling a symbol, a README helps someone start a repository, an architecture decision record preserves a system choice, and a runbook explains how to operate or recover a service.

The practical rule is simple: use deterministic source facts where possible, use AI to organize or explain evidence, and assign a person to own decisions that the source does not reveal.

Separate source facts from intent

Source code, type information, and tests can support a draft of names, signatures, parameter types, return types, declared exceptions, and observable behavior. They do not automatically establish why a design was selected, which alternative was rejected, or what policy governs an edge case. AI may propose an explanation, but an owner must verify that it reflects the real decision.

For example, a payment worker may retry a failed request. Its docstring can describe inputs, returned status, and errors evidenced by the implementation or tests. An ADR should record the decision about idempotency and duplicate charges if that decision exists. A runbook should explain how an authorized operator checks payment state before replaying a failed job. If the retry policy or replay authority is unknown, the engineer must obtain that information rather than let a draft imply an answer.

A lightweight intent record can capture the decision, context, options considered, tradeoffs, consequences, assumptions, owner, and condition for revisiting it. ADR formats vary; the ADR project templates provide examples rather than one mandatory schema. MADR also demonstrates how teams can record alternatives and tradeoffs in a decision record: see its example decision.

Place information where its reader needs it: symbol behavior in a docstring, non-obvious local reasoning in a source comment, system-level alternatives in an ADR, and restart or rollback steps in a runbook. Link canonical records instead of copying the same rationale into several places.

Choose the right documentation destination

Choose a destination by the reader’s task, then maintain one canonical record for that purpose.

Destination	Reader’s task	What belongs there	Update trigger
Comment or docstring	Understand a code path or call a symbol	Non-obvious reasoning, inputs, outputs, errors, side effects, and restrictions	Behavior or interface change
README	Start or contribute to a repository	Purpose, prerequisites, installation, quick start, configuration, contribution path, support, and links	Setup or project change
ADR	Understand a system-level choice	Context, decision, options, tradeoffs, consequences, and revisit conditions	Decision or assumption change
Runbook	Operate or recover a service	Checks, restart or rollback steps, failure handling, escalation owner, and replay safeguards	Process or operational change

Comments should add reasoning rather than translate visible code. Python’s PEP 8 advises using inline comments sparingly. PEP 257 recommends docstrings for public Python modules, functions, classes, and methods, but does not require a docstring for every private helper. A trivial private function may be clearer without one.

Keep a README focused on the basics and link to deeper material. The Diátaxis framework distinguishes tutorials, how-to guides, reference, and explanation. That separation helps prevent a README from becoming an API manual, runbook, and architecture history at once.

ClickUp’s official help documentation describes Brain as supporting drafting, summarizing, organizing, and editing content in Docs and Wikis using workspace context that the user can access. Availability depends on permissions, workspace configuration, and plan. This supports workspace-document assistance, not a verified repository-to-API-reference pipeline. Teams organizing internal documentation can review ClickUp setup and automations for relevant workspace workflow context.

Build a bounded AI drafting workflow

Do not ask for a generic explanation of a file. Define a documentation contract: identify the repository and immutable source revision, name the qualified symbol, provide the signature, existing documentation, relevant tests, runtime or configuration assumptions, and approved notes. Request only fields that the evidence can support, and mark unsupported details as unknown.

The following is a proposed editorial contract, not a schema supplied by GitHub, ClickUp, Sphinx, Javadoc, or JSDoc:

{
  "summary": "string tied to observed behavior",
  "parameters": ["name and evidence-based description"],
  "returns": "description or unknown",
  "raises_or_errors": ["error supported by source or test"],
  "side_effects": ["known effect or none known"],
  "preconditions": ["known requirement or unknown"],
  "examples": [{"status": "executed or illustrative"}],
  "limitations": ["known limitation or unknown"],
  "intent_layer": "approved reference or human input required",
  "source_revision": "commit SHA",
  "evidence_refs": ["source path, symbol, or test"],
  "review_status": "draft"
}

GitHub documents a /doc command in Copilot Chat for adding a documentation comment for a symbol in supported surfaces, and an /explain command for explaining code. GitHub also recommends reviewing and validating Copilot output. These are assistance features, not proof that a comment is correct or automatically published. For teams designing governed workflows, ConsultEvo offers AI agent design and implementation; that page is not evidence of a prebuilt code-documentation pipeline.

Apply the workflow to real documentation changes

The workflow becomes useful when each documentation type has a defined trigger, output, validation rule, destination, and fallback. The following examples are proposed implementation patterns, not vendor-provided templates or tested integrations.

Trigger	AI job	Validation and action	Fallback
Public function changes in a pull request	Draft summary, parameters, returns, errors, side effects, and example status from the changed revision and tests.	Check the signature and tests, run safe examples, then place the approved text in the source docstring and rebuild the reference.	Route missing behavior to the code owner; route payment, retry, or security claims to the accountable service owner.
System design review reaches a decision	Organize approved notes into context, options, tradeoffs, consequences, and revisit trigger.	The decision owner confirms the selected option and consequences before saving the ADR.	Keep the record proposed and list missing decisions; do not let AI select an architectural option.
Documentation build runs for a source revision	Optionally suggest wording for changed documentation only; let deterministic tooling build the reference.	Run syntax and documentation checks in isolation, show the diff, and record artifact revision and review status.	Send import failures to the build owner and content discrepancies to the code owner; do not publish a partial artifact.

For a symbol draft, the input chain can be concrete: repository identifier, commit SHA, source path, qualified symbol, signature, tests, current docstring, and approved context references. The AI output should contain evidence references such as a source path, symbol, or test identifier. A parameter that does not exist in the signature is a hard validation failure. A return claim that contradicts annotations or tests is also a failure, not a wording issue.

For an ADR, the input should be approved notes rather than an unreviewed conversation. The AI can organize the record and identify missing fields, but the decision owner must enter or confirm the decision, alternatives, tradeoffs, consequences, assumptions, and revisit trigger. The canonical destination is an ADR directory or governed internal knowledge base, with links from relevant repository and operational documentation.

For a revision-bound build, keep the source artifact and generated artifact tied to the same commit. If Sphinx is used, sphinx.ext.autodoc imports Python modules to pull their docstrings, and imports can execute side effects. Use an appropriately isolated build environment. For Java, Oracle’s Javadoc documentation describes generating HTML API documentation from declarations and documentation comments. JSDoc’s guide documents JavaScript source documentation. These tools have different language-specific workflows and do not supply missing rationale.

Validate, review, and publish without hiding uncertainty

Use deterministic parsing or language tooling for facts that are already machine-readable: signatures, parameter names, type annotations, visibility, routes, version markers, and deprecation markers. Use AI to draft explanations or organize approved notes. If a response contains a status field, parse it and accept only allowed values such as draft, pending, or approved. Reject unexpected fields when the contract requires a closed structure.

Check that documented parameter names and types match the actual signature. Compare return and error claims with source behavior and tests. Detect removed symbols, stale paths, deprecated APIs, and missing configuration keys. Run examples in an isolated environment where feasible. Label an example as illustrative if it has not been executed, and use synthetic data or sandbox credentials for examples that call external systems.

Use a risk-based gate. Routine wording for a private helper may pass automated checks. A public interface or exception change needs reviewer confirmation. Claims involving security, billing, privacy, deletion, retries, external side effects, compliance, or incident recovery need an accountable subject-matter reviewer and an updated ADR or runbook when relevant.

Protect source code and supporting context according to organizational privacy, security, and data-governance rules. ClickUp’s help documentation states that Brain uses accessible workspace context and that availability depends on permissions and plan. Treat credentials, customer data, regulated information, and proprietary source as controlled inputs rather than ordinary prompt material.

Keep documentation current as code changes

Update documentation in the same pull request as a behavior or interface change where possible, then rebuild generated reference from that revision. A source revision identifies what a draft describes; it does not prove that a person approved it. Keep approval and provenance as separate fields.

Separate the data grain before choosing a key:

• Symbol-level document record: one document for one repository, immutable source revision, source path, qualified symbol, and document kind. A proposed uniqueness key is repository_id, commit_sha, source_path, symbol_qualified_name, document_kind.
• Generation-run record: one processing attempt, additionally identified by run_id, model_id, prompt_version, alongside the repository, revision, path, symbol, and document kind. This permits multiple model or prompt runs for the same source.
• Evidence record: one citation for one document and evidence location. A proposed key is document_id, evidence_uri, evidence_locator, evidence_hash, so multiple citations do not overwrite one another.
• Health summary: one defined metric for one repository scope and reporting period. A proposed key is repository_id, documentation_scope, period_start, period_end, metric_definition, tool_version. Do not use a symbol-event key for a weekly or monthly aggregate.

These are illustrative data-model recommendations, not vendor-published schemas. A date, title, or file path alone can collide across model variants, repeated runs, multiple citations, or changed reporting windows. For concurrent workers, enforce uniqueness in the database and use an atomic upsert or transaction where supported. A lookup-then-insert check can race. Keep an idempotency key stable when retrying the same run, while a new model or prompt run receives a distinct run identity.

Assign an owner to each canonical destination and audit longer-lived material at release boundaries or on a defined schedule. When a decision changes, update the ADR and linked operational instructions. When only an interface changes, update the source documentation and generated reference without copying unrelated rationale into a second record.

Use generators for reference and AI for drafting

Choose a conventional generator when the deliverable is reproducible, language-specific API reference. Javadoc generates HTML API documentation from Java declarations and documentation comments. Sphinx autodoc can pull Python docstrings. JSDoc documents JavaScript source. These are distinct workflows, with different inputs, build behavior, and configuration.

A generator extracts or formats structured source material; it does not establish whether a business rule is correct. AI can draft prose, explain non-obvious control flow, organize human-approved context, or propose a README outline. Keep source-based checks and review around both kinds of output.

Frequently asked questions

Does every function need a docstring?

No universal rule applies. Public APIs normally deserve clear documentation, while a trivial private helper may not. Follow the language and project conventions. PEP 257’s guidance is especially relevant to public Python modules, functions, classes, and methods.

What belongs in an ADR?

Common fields include context, the decision, options considered, tradeoffs, consequences, assumptions, owner, and a condition for revisiting the decision. Teams can choose different formats and add fields that fit their governance.

What should AI document automatically?

Stable mechanical facts are the safest starting point: signatures, parameter names, types, public symbols, common errors supported by evidence, and reference scaffolding. Business rules, security behavior, retry semantics, and undocumented rationale need human confirmation.

Is AI-generated documentation reliable?

It is useful as a draft, not evidence of correctness. Compare it with source behavior and tests, resolve consequential unknowns, and obtain the required review before publication.

How often should documentation be updated?

Update it with the relevant code or process change. Rebuild generated reference from the changed source and review longer-lived ADRs, runbooks, and onboarding material at release boundaries or on a defined schedule.

Can ClickUp Brain generate an API reference from a repository?

The verified ClickUp Help pages describe drafting and editing Docs and Wikis with accessible workspace context. They do not verify an automatic repository-to-API-reference pipeline. Use a language-specific generator for reproducible reference output and treat workspace drafting as a separate documentation activity.