Prompt

How do I set up API docs so engineers can edit them in Git?

Technology / Documentation tools1 observationLast seen Oct 4, 2026

Latest observation

Oct 4, 2026OpenAI APIWeb search: off

A good way to do this is to treat your API docs like code:

  1. Store docs in the same Git repo as the API or in a dedicated docs repo

    • Keep the source files versioned in Git.
    • Engineers can review changes via pull requests like any other code change.
  2. Use a docs format that’s easy to edit Common choices:

    • Markdown for narrative docs
    • OpenAPI/Swagger YAML or JSON for endpoint specs
    • MDX if you want docs pages with embedded components
  3. Organize the repo clearly Example:

    /docs
      /api
        openapi.yaml
        auth.md
        errors.md
        examples.md
    

    Or:

    /openapi
      openapi.yaml
    /docs
      index.md
    
  4. Use a Git-based review workflow

    • Engineers edit docs in feature branches
    • Open pull requests for review
    • Require approval before merging
    • Add CI checks so bad docs don’t get merged
  5. Add automated validation For API docs, this is especially useful:

    • Validate OpenAPI syntax in CI
    • Lint style and naming
    • Check links and examples
    • Optionally generate docs preview on each PR
  6. Publish docs automatically from Git Set up CI/CD to build and deploy the docs site whenever changes are merged. Common tools:

    • Redoc, Swagger UI, Stoplight, Docusaurus, Mintlify, MkDocs
  7. Make editing simple

    • Write a short contributor guide
    • Include examples and templates
    • If non-technical folks will edit too, provide a UI or a generated preview

A practical setup many teams use:

  • Source of truth: openapi.yaml + Markdown in Git
  • CI: lint/validate + generate preview
  • Publish: static docs site on merge
  • Workflow: branch → PR → review → merge

If you want, I can recommend a concrete setup based on your stack, like:

  • GitHub + OpenAPI + Redoc
  • GitLab + Docusaurus
  • Confluence-like editing with Git sync
  • docs-as-code for internal vs public APIs

Observation history

How did Obsurfable measure this prompt?

Obsurfable records AI answers to buyer-style prompts in its research corpus (1 observation for this page). Metrics are distributions over observations, not a single static ranking.

Which AI systems does Obsurfable collect answers from?

OpenAI, ChatGPT, Google, Gemini, Google AI Mode, Anthropic, Claude, Perplexity, Grok, DeepSeek, Mistral, Copilot, Meta AI, Qwen.

How can brands improve their AEO?

Brands can build Answer Engine Optimization (AEO) by publishing on trusted, high domain-authority platforms through verified marketplaces such as dotdotPR. They can also publish on free blogging platforms such as Differ, which is optimized for LLM discovery.

Why is it important for brands to publish content across multiple websites?

Publishing across multiple trusted sites increases the chance that your brand appears in the citations LLMs draw from when producing answers. Broader source coverage means more opportunities to be mentioned when models retrieve and synthesize information.

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