ModelRefs / AI Documentation Generation — AI Glossary

AI Documentation Generation — AI Glossary

Using LLMs to automatically write or update code documentation, docstrings, README files, and API references.

Overview

Documentation generation (GitHub Copilot docs, Mintlify Writer, Codeium Explain) reads code and produces natural language descriptions. For docstrings: parameters, return values, exceptions, and usage examples. For README: installation, usage, and architecture summaries. Quality depends on code clarity; models excel at inferring intent from well-named identifiers.

Reference details

Topic	applications
Last reviewed	2026-06-24

How it works

The target symbol is located and its source extracted along with its signature, type annotations, and any raised exceptions visible in the body.
Context is widened to callers and callees, because the intent of a function is usually more legible from its use sites than from its own body.
A structured prompt requests the specific sections the project's convention requires — parameters, return value, raises, example — rather than prose about the function.
Generation is constrained to the facts present in the extracted source, since a model asked to document a function it cannot fully see will supply plausible parameter descriptions instead.
Output is inserted syntactically rather than textually, parsing the file and attaching the docstring to the declaration so formatting stays valid.
A verification pass re-reads the committed docstring against the signature, which is what catches parameter names that have since been renamed.

Example: The generation cost is negligible and the review cost is not

A codebase has 1,200 public functions of which 340 carry docstrings, so coverage is 340 / 1,200 ≈ 28.3% and 860 are undocumented. Generating all 860 at 600 input and 150 output tokens each is 516,000 input tokens and 129,000 output tokens, which at $3 per million input and $15 per million output is 0.516 x $3 = $1.55 plus 0.129 x $15 = $1.94, about $3.48 in total. The human comparison looks overwhelming: 860 docstrings at six minutes each is 5,160 minutes, or exactly 86 hours, and at a $75 loaded hourly rate that is $6,450. Stated that way the ratio is $6,450 against $3.48, roughly 1,850 to 1, and it is the wrong number to plan with. Generated documentation has to be read before it is trusted, because a confidently wrong docstring is worse than an absent one — it becomes the thing future readers rely on. Reviewing 860 generated docstrings at 45 seconds each is 645 minutes, or 10.75 hours, which at $75 is $806.25. The honest comparison is therefore $6,450 against $3.48 + $806.25 = $809.73, a factor of about 8.0. That is still a strong result, and it is a different kind of result: the saving comes from converting authoring into reviewing, so it holds only as long as the review actually happens.

Commonly confused with

Documentation generation is not the same as explaining code to a reader, though the same model does both. An explanation is produced on demand for one person who already has a question; documentation is committed to the repository and read by everyone afterwards, which means it inherits a maintenance obligation the explanation does not. This is the distinction that decides where the technique pays: generating a docstring is cheap, and keeping 1,200 generated docstrings true as the code changes is the actual cost. It is also not a substitute for the documentation that cannot be derived from code. A model reading a function can describe what it does; it cannot recover why the approach was chosen, what was tried and abandoned, or which constraint the odd-looking branch exists to satisfy — and those are the parts of a codebase that readers most need written down.

When to use it

Reach for it when:

For mechanical API surface — parameters, returns, raises — which is derivable from the signature and tedious to write by hand.
On well-named code, where the model can infer intent from identifiers rather than guessing at it.
As a first draft for human review, which is the arrangement the roughly 8x honest saving above actually describes.
In a verification pass over existing docstrings, where comparing committed text against current signatures catches drift cheaply.

Reach for something else when:

For design rationale, which is not present in the code and will be confabulated if requested.
Committed without review, since a wrong docstring is relied upon in a way that a missing one is not.
On unclear or badly named code, where the generated description tends to restate the implementation rather than explain it.
As a coverage metric to maximise, which rewards generating 860 docstrings nobody reads over fixing the twenty that mislead.

Continue your research

Use these connected ModelRefs sections to compare alternatives, inspect implementation paths, and review the evidence and governance boundaries relevant to AI Documentation Generation — AI Glossary.

Frequently asked questions

What is AI Documentation Generation?