How to Generate Documentation from Your Codebase with AI

To generate documentation from a codebase with AI, parse the code into a dependency graph first, add git history for ownership and churn, then render pages from that structure and let a model write prose only where explanation matters. Regenerate only the pages whose inputs changed, on every merge. A model that sees one file's text can summarize it. A model that is handed the graph and the history can say why the file matters.

Part of the Auto Wiki guide.

Which approach fits

Five kinds of tool produce documentation from code. They differ in what they read and in how they stay current (as of October 2026).

For a tool-by-tool comparison, see codebase documentation tools compared and repowise vs DeepWiki.

The eight steps, in order

These apply whichever tool you use.

1. Parse every file into a syntax tree. List functions, classes and imports. This is cheap and needs no model.
2. Build the dependency graph. Resolve imports into edges. Files that many others import are the ones whose docs matter most.
3. Mine git history. Record who changes each file, how often, and which files change together. Teams rarely write this down.
4. Render the structural pages. File lists, dependencies and owners come straight from steps 1 to 3.
5. Write the explanatory pages. What a subsystem is for and how data flows come from a model that receives the structure as input, not raw text.
6. Draw diagrams from the graph. Rendered from edges, they cannot drift from the code.
7. Track freshness. Store a hash of what each page was built from, and refresh only pages whose hash changed.
8. Serve it. A browsable site for people, and an MCP server (Model Context Protocol, the standard way coding agents call external tools) so agents read the same pages.

Why hand-written docs drift

Stale documentation is worse than none, because people trust it. A page is accurate the day it is written and wrong the day after a refactor merges. Nobody owns the upkeep, and the context readers most need (why a decision was made, which files are brittle, who to ask) is the context that never gets written.

Generation fixes the first two problems by deriving the reference from the code. Git history fixes the third, because ownership and co-change are recorded in commits whether anyone documents them or not.

Why the input matters more than the model

Prompting a model with raw source produces a restatement of the code. Prompting it with structure produces an explanation. repowise prompts model-written pages with assembled material: parsed structure and signatures, what the subject imports and what imports it, ownership and churn, the significant commits from its history (merges, dependency bumps and lint-only commits filtered out), and the pages it should link to.

That is the difference between "this is a login function" and "this is the authentication entry point, two modules depend on it, and it changed nine times this quarter."

Generate docs with repowise

repowise is open source (AGPL-3.0) and runs locally or in CI. It builds the wiki as part of indexing. The architecture page shows how the parser and the page generator fit together.

pip install repowise
cd /path/to/your/repo
repowise init

init parses the repository, builds the graph, mines git history and writes the wiki. It parses 28 languages to a full syntax tree. In a terminal it asks which model provider to use; exclude paths with -x, for example -x vendor/ -x 'src/generated/**'.

No API key needed

repowise init --no-prose

--no-prose is free and guaranteed to spend nothing. Almost the whole wiki is rendered from structure, so a keyless index is a complete wiki, not a placeholder. File pages, symbol pages, dependency cycles, infrastructure files, hotspots, ownership, dead code and decision archaeology are identical with or without a key. A key adds model-written prose to three page types: subsystem pages, the repository overview and the onboarding pages.

What gets generated

The repository overview carries a system map drawn from the graph: structure picks the boxes and arrows, and a configured model only names them.

repowise wiki for the repowise repository: a docs tree with Getting Started, Key Concepts and How It Works, and the Repository Overview page with a project summary and a table of contents

Each file page puts the dependency edges, ownership, code health and tests next to the description.

A repowise file page for the language specification dataclass in the repowise repository: 59 files depend on it, with code health, history, decisions, dependencies and tests tabs

You can browse generated output on real repositories in explore, including the FastAPI docs and its ownership map, before installing anything.

Choose a voice

A style changes the prose without changing headings, so search and links keep working.

repowise init --wiki-style reference
repowise restyle tutorial      # re-render every page in a new voice

Custom styles live in .repowise/styles/<name>/style.yaml. language in .repowise/config.yaml (or init --language <code>) sets the natural language of the wiki; code, paths and symbol names are never translated.

Keep it fresh on every merge

repowise update re-renders only what your commits touched. It finds the files changed since the last sync, walks the dependency graph for the pages those changes affect, and rebuilds that handful, typically a few per commit.

repowise update                 # incremental, fast enough for every merge to main
repowise generate --stale       # rewrite pages whose code changed since they were written
repowise generate               # fill in subsystem prose you skipped at init

Two checks decide what is stale. A model-written page compares a hash of its rendered prompt with the stored one: same prompt and same model means the prior page is reused, with no tokens spent. A structure-rendered page folds the subject's hash together with a fingerprint of the renderer, so a template improvement triggers exactly one regeneration. Every page carries a status of fresh, stale, expired or unknown, which gives you a list to act on instead of a feeling that the docs are old. For wiring this into CI, see keeping documentation fresh.

Serve it to people and agents

repowise serve opens the wiki locally, and repowise export --format markdown writes it out as files. The same pages power the ten MCP tools, so Claude Code, Cursor, Codex and other agents query architecture, file pages and dependencies directly instead of reading files one at a time. See how to generate a CLAUDE.md for the agent-facing side.

What to generate and what to write

AI replaces the reference layer, not the reasoning layer.

Pitfalls

• Generating from raw file text. The output restates the code and misses how files relate. Check that your tool builds a graph first.
• Regenerating everything on a schedule. It wastes tokens and churns diffs. Rebuild by hash, on merge.
• Treating prose as extraction. A structure-rendered page is extracted and reports confidence 1.0; a model-written page is a grounded summary and reports 0.8. Read them accordingly.
• Leaving refresh as a manual step. If it is not automated, it will be skipped within a month.