Guides · Updated 2026-10-23

Best practices for code documentation

Nobody reads documentation for fun, so every line has to earn its place. Good code documentation answers the questions a reader has at the moment they have them: What does this function expect? What do I get back? What happens on failure? Why is this designed this way?

This guide covers the conventions that work - JSDoc for JavaScript/TypeScript, docstrings for Python, READMEs for repositories - what to skip, and how to keep documentation true as code changes. You can draft documentation for any snippet in seconds with the free documentation generator.

Document the contract, not the implementation

The highest-value documentation describes the contract: what a caller may rely on. For every public function, class or endpoint, that means:

Purpose - one sentence, in terms of the caller's problem.
Parameters - types, units, valid ranges, defaults.
Return value - shape and meaning, including the empty/none case.
Failures - which errors it throws or returns, and when.
One example - a realistic call with its result; examples are read ten times more than prose.

Implementation details belong in code comments only when the why is non-obvious - see the clean code guide on comments.

JSDoc that pulls its weight

For JavaScript and TypeScript, JSDoc feeds editors, type checkers and doc sites at once:

/**
 * Splits an amount into equal shares, assigning the rounding
 * remainder to the first share so the total is always exact.
 *
 * @param {number} amountPaise - Total in paise (not rupees).
 * @param {number} ways - Number of shares, >= 1.
 * @returns {number[]} Shares in paise, summing to amountPaise.
 * @throws {RangeError} If ways < 1 or amountPaise < 0.
 * @example
 * splitAmount(1001, 3) // [335, 333, 333]
 */

Notes that make JSDoc useful rather than ceremonial: document units in the parameter description, document the rounding/edge behavior (that is what people come looking for), and in TypeScript skip type annotations JSDoc would duplicate - keep the prose.

Python docstrings: pick a style and stay with it

PEP 257 sets the frame; Google style is the most readable of the common formats:

def split_amount(amount_paise: int, ways: int) -> list[int]:
    """Split an amount into equal shares with an exact total.

    The rounding remainder goes to the first share, so
    sum(result) == amount_paise always holds.

    Args:
        amount_paise: Total in paise (not rupees).
        ways: Number of shares, at least 1.

    Returns:
        Shares in paise, largest first.

    Raises:
        ValueError: If ways < 1 or amount_paise < 0.
    """

The first line is a complete sentence that fits on one line - tools like help(), IDE hovers and documentation generators show it alone. Type hints belong in the signature, not repeated in prose.

The README is the front door

A repository README answers four questions in order: what is this (two sentences), how do I run it (copy-pasteable commands that actually work), how do I use it (one real example), and where do I go deeper (links). Everything else - architecture notes, decision records, contribution rules - links out rather than piling in. Draft and preview yours in the free Markdown editor, which renders code blocks with syntax highlighting as you type.

Keep documentation honest

Wrong documentation is worse than none - readers trust it over the code and ship the bug. What keeps docs true:

Docs change in the same commit as behavior. A diff that changes a default without touching the docstring is an incomplete diff - reviewers should block it.
Examples are tests: Python doctests and example-based unit tests fail when the documented behavior drifts. The test generator happily turns an @example into cases.
Date your prose docs ("Updated 2026-10") so readers can calibrate trust.
Delete dead docs: a paragraph about a removed flag costs every future reader a detour.

Let AI write the first draft, you write the truth

Documentation has a blank-page problem, and AI solves exactly that part. Paste a function into the documentation generator and you get idiomatic JSDoc or docstrings - summary, parameters, returns, raises, example - in seconds. Then do the human pass: correct the intent ("why" is yours, the AI only sees "what"), check the edge cases it inferred, and cut anything that merely restates the signature. Ten minutes of editing a complete draft beats an hour facing an empty comment block - and the result is documentation that actually exists.

Generate documentation free with AI

Frequently asked questions

What should code documentation include?

The contract: purpose in one sentence, parameters with types and units, return value including the empty case, errors thrown, and one realistic example. Skip anything that restates the code.

JSDoc or TypeScript types - do I need both?

In TypeScript, let the type system carry types and use JSDoc for what types cannot say: units, valid ranges, edge-case behavior, and examples. In plain JavaScript, JSDoc carries both.

Which Python docstring format is best?

Any of Google, NumPy or Sphinx style works - consistency matters more than the pick. Google style is the most readable in plain text and is what most new projects choose.

Can AI generate code documentation?

Yes - paste code into the free generator at code.doaide.com/tools/generate-docs and get JSDoc, docstrings, Javadoc or godoc comments with parameters, returns and examples. Review the draft: AI documents what the code does, you confirm what it is for.