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Treating documentation like software isn’t just a trend, it’s a fundamental shift in how teams create and maintain high quality content. The core of this shift is the docs as code workflow, a methodology that applies software development best practices to documentation. By adopting this approach, you can dramatically improve collaboration, automate quality checks, and ensure your docs are always in sync with your product.
This guide breaks down the entire process, from foundational principles to advanced automation, giving you a clear roadmap for implementing a modern docs as code workflow.
Before diving into tools and automation, it’s important to understand the philosophy. A true docs as code workflow is built on a few key ideas that make everything else possible.
At its heart, docs as code is a simple philosophy: treat documentation with the same rigor and tooling as your application code. This means storing content in a version control system like Git, writing in plain text formats, and using automated processes for reviews, testing, and deployment. The Write the Docs community defines it as using the same tools for docs as you do for code, which fosters accountability and integrates writers directly into the development lifecycle.
The first practical step is putting your documentation into a version control system, usually Git. This is non negotiable. Storing docs in Git provides a complete history of every change, including who made it, when, and why. According to Atlassian, version control is essential for tracking every modification and enabling safe rollbacks, making collaboration seamless and preventing conflicting edits.
Forget proprietary formats. A docs as code workflow relies on plain text markup languages like Markdown, reStructuredText (reST), or AsciiDoc. Writing in plain text makes your content easy to read, edit, and most importantly, “diffable”. This means you can see the precise changes between two versions, which is critical for effective reviews in a pull request. The widespread adoption of the CommonMark specification by platforms like GitHub and GitLab has made Markdown a go to choice for its simplicity and power. To keep things DRY, define reusable Markdown snippets you can include across pages as your docs grow.
A core benefit of this approach is separating your raw content (the Markdown files) from how it’s displayed (the website’s theme and styling). Tools like static site generators handle this separation beautifully. For example, Jekyll uses layouts to wrap content, and Hugo has a specific directory structure that keeps content, layouts, and static assets neatly organized. This allows you to completely redesign your documentation site without ever needing to rewrite the content itself.
With the foundational principles in place, the next step is to set up a local environment where writers can work efficiently.
Static site generators are the engines of a docs as code workflow. They take your plain text files, combine them with templates, and generate a complete static website (HTML, CSS, and JavaScript). These sites are incredibly fast, secure, and easy to deploy. Popular SSGs for documentation include Docusaurus, MkDocs, Hugo, and Jekyll. According to MDN, SSGs are a common choice for documentation because they compile content into static sites that can be served efficiently from a Content Delivery Network (CDN).
One of the biggest wins for authoring speed is the ability to preview changes locally before committing them. Most SSGs come with a built in development server that watches for file changes and automatically reloads your browser.
Docusaurus provides npm run start to spin up a local server.
MkDocs uses the simple mkdocs serve command for live reloading.
Sphinx users can run sphinx-autobuild for a similar hot reload experience.
This tight feedback loop lets you write, build, and check your work in seconds, not minutes. If you use Fern, you can preview your docs locally with the Fern CLI and iterate quickly before committing.
This is where the “code” part of docs as code truly comes to life. The process for changing documentation mirrors the battle tested workflow used by software engineers.
Instead of emailing documents or editing directly in a CMS, changes are proposed through pull requests (PRs). A pull request is a formal proposal to merge changes from one branch into another. It shows a clear comparison (a “diff”) of the before and after, allowing teammates to review every single line that was added, removed, or modified. You can even create a “draft” PR to get early feedback before it’s ready for a formal review. Fern’s Visual Editor lets non‑developers propose changes that automatically open a GitHub pull request, so your review workflow stays intact.
Docs are reviewed with the same seriousness as code. Reviewers can leave comments on specific lines, ask questions, and suggest improvements directly within the pull request. Platforms like GitHub allow you to require a certain number of approving reviews before a PR can be merged. This process ensures that all feedback is addressed before changes go live. Microsoft’s public documentation process, for example, requires a core content team member to approve PRs, enforcing a consistent editorial standard.
The docs as code workflow breaks down silos between engineering and technical writing teams. Since everyone is using the same tools (Git, GitHub, etc.), collaboration becomes natural. Engineers can easily contribute to documentation for features they build, and writers can review those changes. Using a CODEOWNERS file in GitHub can even automatically request reviews from the technical writing team whenever files in the /docs/ directory are changed.
Documentation work should be as visible as any other engineering task. By integrating with issue trackers like Jira, Linear, or GitHub Issues, you can link documentation tasks directly to the pull requests that resolve them. You can use keywords like “Closes #123” in your PR description to automatically link the PR to an issue and close it upon merging.
Automation is what makes the docs as code workflow truly scalable and reliable. By setting up automated checks and deployment pipelines, you can catch errors early and publish updates effortlessly.
Just as code has unit tests, docs can have automated quality checks.
Prose Linters: Tools like Vale can check your writing against a configurable style guide, catching everything from passive voice to inconsistent terminology.
Markdown Linters: markdownlint enforces consistent formatting for headings, lists, and line length.
Link Checkers: Tools like lychee can automatically scan your entire site for broken links, preventing dead links from ever reaching production.
API Spec Linters: For API docs, tools like Redocly CLI can lint your OpenAPI specification to enforce style rules and catch structural issues.
Continuous Integration is the practice of running automated builds and tests on every single commit. Using a CI platform like GitHub Actions, you can create a workflow that triggers on every pull request. This workflow can build your documentation site, run all your linters and tests, and report back whether everything passed. You can configure your repository to block merging a pull request until all these required CI checks have succeeded.
Preview environments are a game changer for reviews. Services like Netlify and Vercel can automatically build and deploy every single pull request to a unique, shareable URL. A link to this “deploy preview” is then posted back to the PR. This allows reviewers to see exactly how the rendered changes will look on the live site, rather than just reading raw Markdown. It’s an essential part of a modern docs as code workflow. Teams running GitLab can also wire up CI to auto‑build documentation previews for each change.
Once a pull request is approved and merged into the main branch, Continuous Deployment takes over. This is an automated process that builds the final version of your documentation site and publishes it to your production environment. Vercel’s Git integration, for instance, automatically triggers a production deployment when changes are merged to the main branch. This means your docs are always up to date, with no manual publishing steps required. When you’re ready to go live, point your site to a custom domain (subdomain, subpath, or root) in just a few steps.
Once you have the basics down, you can incorporate more advanced techniques to handle complexity and keep your content accurate.
If your product has multiple versions, your documentation needs to reflect that. Fern supports multi‑version documentation with a simple configuration and plan‑based limits, so users can switch between versions like v1 and v2 without friction. Docusaurus has a built in versioning workflow, and MkDocs can use a plugin called mike to manage and deploy multiple versions of a site. This allows users to easily switch between documentation for v1.0, v2.0, and so on.
Keeping docs and code perfectly aligned is the ultimate goal. A docs as code workflow enables this through several methods.
Autodocs: Tools like Sphinx’s autodoc extension can pull documentation directly from docstrings in your source code, eliminating drift.
Required Checks: As mentioned, you can use branch protection rules to require that documentation builds and previews succeed before code can be merged.
CODEOWNERS: Automatically loop in writers for review on PRs that touch both code and related documentation files.
Make your docs agent‑friendly by auto‑publishing llms.txt and llms‑full.txt so AI tools can reliably consume your content.
Implementing a robust docs as code workflow is critical for any company with a public API. It’s the difference between documentation that developers love and documentation that causes frustration.
Platforms like Fern are built from the ground up on these principles. Fern helps teams generate beautiful, interactive API documentation and idiomatic SDKs directly from an API specification, ensuring they are never out of sync. By embracing a Git native docs as code workflow with features like preview deployments and versioning, Fern handles the complex engineering work so your team can focus on creating a world class developer experience.
If you’re ready to modernize your documentation process and deliver docs developers are proud of, explore Fern’s docs as code platform.
What is the main benefit of a docs as code workflow?
The primary benefit is improved documentation quality and consistency. By using version control, code reviews, and automated testing, you catch errors before they are published and ensure a high standard for all content. It also dramatically improves collaboration between writers and developers.
Can non developers use a docs as code workflow?
Absolutely. While the tools (Git, Markdown) originated in software development, they are becoming increasingly common. With good onboarding and modern tools like GitHub Desktop, the learning curve is manageable for technical writers and other content contributors.
What are the best tools for a docs as code workflow?
The ideal toolkit usually includes Git for version control, a plain text editor like VS Code, a static site generator (Docusaurus, MkDocs, Hugo), and a CI/CD platform (GitHub Actions, Netlify, Vercel). Linters like Vale and markdownlint are also highly recommended for quality control.
How does this workflow help with API documentation specifically?
A docs as code workflow is exceptionally powerful for API documentation. You can store your OpenAPI (or Fern) definition in the same repository as your docs. With Fern, you can generate an API Reference from your OpenAPI or Fern definition with a single line in docs.yml. Automated tools can then validate the spec, generate code samples, and build the documentation site in a single, unified pipeline, ensuring your API reference is always accurate.
Is it difficult to set up an initial docs as code workflow?
Setting up a basic workflow is straightforward. Most static site generators have excellent getting started guides. Platforms like Vercel and Netlify can connect to a GitHub repository and configure a complete CI/CD pipeline with just a few clicks, including free preview deployments for every pull request. The initial setup pays for itself quickly through time saved and quality gained.
