Writing Technical Documentation with Claude

1. Why AI-assisted documentation makes sense

Technical documentation is one of those tasks that regularly gets pushed back in Magento projects. A new module is finished, tested, and deployed, but the README stays empty or contains only a placeholder, because the actual feature work had priority. Claude changes that calculation by drastically lowering the activation barrier: instead of staring at a blank page, you get a structured draft in a few minutes that is derived from the actual code. That does not replace documentation work, but it shifts the effort from writing to reviewing and refining, which for most developers is the far more pleasant task.

Concretely, for a module like Mironsoft_SeoSuite, that means giving Claude access to the model classes, the Api/Interfaces, and the etc/ configuration, and getting back a structured overview of purpose, configuration paths, and extension points. The value is not that the output is perfect, but that it serves as a concrete basis for discussion. Correcting an existing text is simply easier than formulating from nothing what a module actually does.

2. Gathering context: code, tests, and history as the foundation

The quality of a generated documentation draft depends almost entirely on the quality of the context provided. If you only give Claude the name of a class, you get plausible-sounding but often wrong assumptions back. If instead you provide the relevant PHP classes, the corresponding PHPUnit tests, the system.xml, and the most recent meaningful commit messages, you get a draft that actually describes what the code does rather than what it could plausibly do. Tests are particularly valuable here because they make intended behavior explicit, while the implementation code sometimes only shows the current, not necessarily the intended, state.

Claude Code has a structural advantage over the plain chat interface here: it can navigate the repository on its own, pull in related files, and search for usage sites with grep instead of relying on manually copied snippets. A project-wide CLAUDE.md with conventions on namespaces, configuration paths, and the dual-vendor workflow provides additional, consistent background without having to re-explain it on every request. One boundary still matters: entire vendor directories or files containing credentials do not belong in the context, since they neither improve doc quality nor belong there in the first place.

3. Generating the first version: prompting strategies

A good prompt for documentation generation fixes three things: the audience, the expected structure, and the boundary of what the model is allowed to invent. Audience means concretely: is the text for developers who will extend the module, or for store operators looking for a configuration option? Both need different depth and different vocabulary. Structure means specifying fixed headings, such as purpose, installation, configuration, and extension points, instead of letting Claude decide on its own what matters.

The most important piece is the explicit instruction to flag uncertain claims and invent nothing that cannot be verified in the code. Without that instruction, language models tend to fill gaps with plausible-sounding but wrong details, such as a configuration path that is close to real but not exact. With the instruction, Claude marks such spots with TODO: verify, which speeds up the subsequent review considerably, because you can check specific spots instead of scrutinizing every sentence equally hard.

#!/usr/bin/env bash
# Draft a module README with Claude Code from inside the module directory
cd src/app/code/Mironsoft/SeoSuite

claude -p "Read every PHP file in Model/, Api/, Block/ and the etc/ directory.
Draft a README.md for this Magento 2 module covering:
1. Purpose (one paragraph, based only on what the code actually does)
2. Installation (composer require + setup:upgrade)
3. Configuration (list every system.xml path with its default value)
4. Extension points (plugins, observers, preferences this module defines)
Mark every claim you cannot verify from the code with 'TODO: verify'.
Do not invent config paths or class names that are not present in the files."

4. Practical workflow: building a module README

A solid workflow treats Claude's output like any other pull request proposal: generate, diff, review, and only then commit. The first step is a dedicated branch, so the generated draft stays isolated and does not accidentally mix with other changes. The second step is the actual generation, ideally with a narrow scope such as a single module directory, so Claude does not attempt to derive project-wide statements from incomplete context.

The decisive step comes after that: the diff is read like code, not skimmed like prose. Every claim about configuration paths gets checked against the real system.xml, ideally with an automated comparison rather than manual lookup. Only once that comparison is clean does the commit happen, with a commit message that makes clear this is a reviewed, generated draft. That label is not just formality, it helps later readers of the git history understand how the text came to be.

#!/usr/bin/env bash
# Full workflow: generate, review, and commit a module README
git checkout -b docs/seosuite-readme

# 1. Let Claude Code read the module and draft the README
claude -p "Draft README.md for app/code/Mironsoft/SeoSuite based on the current code only" \
  > /dev/null

# 2. Inspect exactly what changed before trusting it
git diff --stat
git diff README.md

# 3. Verify every documented config path against the real system.xml
bin/magento config:show mironsoft_seosuite \
  | diff - <(grep -oP '(?<=<field id=")[^"]+' etc/adminhtml/system.xml)

# 4. Only commit after a human has read every paragraph
git add README.md
git commit -m "docs: add generated README for SeoSuite module (reviewed)"

5. Human review: securing accuracy and tone

The review requirement is not a formality, it is the actual core of a serious workflow around AI-generated documentation. Language models do not hallucinate randomly, they hallucinate in a way that is particularly dangerous: the text sounds competent and internally consistent, even when it contains wrong method names, outdated configuration paths, or classes that do not exist. That very persuasiveness is exactly why review is necessary, since an obviously bad text would stand out anyway, a subtly wrong one would not.

Beyond the factual check, tone and audience need a review pass too. Depending on the prompt, Claude tends toward slightly promotional or unnecessarily verbose language that has no place in technical documentation. A reviewer should specifically look for superlatives and vague phrasing and replace them with concrete, verifiable statements. In addition, a check for sensitive information belongs in the process: internal server paths, client names, or test credentials that accidentally slipped from the context into the generated text must never leave a repository.

6. Keeping documentation and code in sync

The real problem with technical documentation is rarely creation, it is maintenance over time. A README generated once is potentially already outdated after the first significant refactor, and outdated documentation is in many cases worse than none at all, because it creates false confidence. A simple but effective approach is an automated comparison between documented method names and the signatures actually present in the code, running as part of the continuous integration pipeline and failing on mismatch.

A script like that does not replace a content review, but it catches the coarse category of drift where a public method was renamed or removed without the docs being updated. For deeper changes, such as altered business logic, a human stays responsible. In practice it works well to give Claude only the diff between old and new code as context after a pull request and ask for a targeted update, rather than regenerating the entire README every time and losing manually inserted nuance in the process.

#!/usr/bin/env python3
"""Check if documented public methods still exist in the actual PHP class."""
import re
import sys
from pathlib import Path


def extract_documented_methods(readme_path: Path) -> set[str]:
    text = readme_path.read_text(encoding="utf-8")
    return {m.rstrip("()") for m in re.findall(r"`([a-zA-Z]+\(\))`", text)}


def extract_real_methods(php_path: Path) -> set[str]:
    text = php_path.read_text(encoding="utf-8")
    return set(re.findall(r"function\s+([a-zA-Z_]+)\s*\(", text))


def main() -> int:
    readme = Path("app/code/Mironsoft/SeoSuite/README.md")
    php_file = Path("app/code/Mironsoft/SeoSuite/Model/MetaGenerator.php")

    documented = extract_documented_methods(readme)
    real = extract_real_methods(php_file)

    stale = documented - real
    if stale:
        print(f"[DRIFT] Documented but no longer present: {sorted(stale)}", file=sys.stderr)
        return 1

    print("[OK] Documentation matches current method signatures")
    return 0


if __name__ == "__main__":
    sys.exit(main())

7. Claude Code and CI: automating doc maintenance

Automation here should not mean that doc updates get committed unattended, it should mean that humans get reliably reminded when a change likely requires a doc update. A hook in Claude Code can print a reminder after every edit to files in an Api/ directory, suggesting the corresponding README be checked. That costs no extra interaction, since it piggybacks on a step that happens anyway, and prevents an interface change from slipping past the documentation unnoticed.

In the CI pipeline the same idea can be applied at the pull request level: a script checks whether a PR changes files in Api/ without touching the README, and if so, has the Anthropic API draft a short, concrete comment asking the author to double check the docs. It matters that this mechanism only produces a hint, not an automatic change, since an automatically changed doc without review would be exactly the problem this workflow is meant to avoid.

{
  "hooks": {
    "PostToolUse": [
      {
        "matcher": "Edit|Write",
        "hooks": [
          {
            "type": "command",
            "command": "bash -c 'echo \"$CLAUDE_TOOL_INPUT\" | grep -q \"Api/\" && echo \"Reminder: update README.md, this change touched a public interface\" >&2 || true'"
          }
        ]
      }
    ]
  }
}

// scripts/check-docs-drift.mjs: flags PRs that change public API without touching README.md
import Anthropic from "@anthropic-ai/sdk";
import { execSync } from "node:child_process";

const anthropic = new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY });

const changedFiles = execSync("git diff --name-only origin/main...HEAD")
  .toString()
  .trim()
  .split("\n");

const touchedApi = changedFiles.some((f) => f.includes("/Api/"));
const touchedDocs = changedFiles.some((f) => f.endsWith("README.md"));

if (touchedApi && !touchedDocs) {
  const response = await anthropic.messages.create({
    model: "claude-sonnet-4-5",
    max_tokens: 300,
    messages: [
      {
        role: "user",
        content: `This PR changes files in an Api/ directory but not README.md. Files changed: ${changedFiles.join(", ")}. Write a short PR comment asking the author to confirm the docs are still accurate.`,
      },
    ],
  });
  console.log(response.content[0].text);
  process.exitCode = 1;
}

8. Limits and risks of AI-generated documentation

Claude only knows what can be derived from code, tests, and the text sources it is given. Business decisions that are not in the code, such as why a particular threshold for discount tiers was chosen, stay invisible and can at best be flagged as an open question, never correctly answered. With complex plugin chains involving several interceptors on the same method, the model can misjudge the actual execution order, because that depends on the sequence configuration spread across multiple di.xml files that are not always fully present in the context.

A second, often underestimated risk is confidentiality: anyone sending internal, client-specific code to a hosted service needs to know the data processing terms and any contractual agreements with the client before sharing source code or configuration details. Claude Code, which accesses files locally, differs from the plain API in terms of data flow and should be assessed accordingly. As a general rule: a convincing-sounding but wrong documentation text is more dangerous than a visible gap, because readers distrust a gap but tend to trust fluent prose.

9. Good vs. bad AI-generated documentation compared

The table below sets the most common pitfalls of AI-assisted documentation against the recommended countermeasures. The common denominator is always the same: a generation step alone is not enough, a defined review and maintenance step has to follow it.

It stands out that almost all risky patterns in the left column trace back to the same mistake: treating the generation step as an endpoint instead of an intermediate step. Anyone who treats doc generation from the start as a proposal that goes through review, testing, and maintenance avoids most of these pitfalls without any extra tooling.

10. Summary

Claude solves the actual activation problem of technical documentation: the jump from a blank page to a structured draft derived from the code. Anyone who provides model files, Api/Interfaces, tests, and configuration as context, and explicitly has uncertain claims flagged, gets a draft that works as a basis for discussion and considerably shortens the actual writing work. That is the core benefit, and it is measurable in real time saved.

The benefit tips over, though, as soon as the generated text is treated as finished without review. Accuracy, tone, and ongoing synchronization with the actual code remain human responsibility, which is best cast into fixed workflow steps: read the diff, compare configuration paths automatically, anchor doc updates as a mandatory part of the PR process. Anyone who consistently follows these steps gets a genuine productivity gain from Claude without sacrificing the reliability of the documentation.

11. FAQ: Writing Technical Documentation with Claude