Prompt
How do I set up a technical documentation platform for docs and tutorials with version control and SME review cycles?
Latest observation
Here’s a practical way to set up a technical documentation platform for docs + tutorials with version control and SME review cycles.
1) Define the documentation model
Start by separating your content into a few clear types:
- Reference docs: APIs, product behavior, configuration, troubleshooting
- Tutorials / learning paths: step-by-step, task-based, outcome-oriented
- Guides / how-tos: “how to do X”
- Release notes / changelogs: version-specific updates
- Internal SME notes: optional, not always published
This helps you build workflows, templates, and review rules per content type.
2) Choose the platform architecture
A good modern setup is usually:
Option A: Docs as code
Best for engineering-heavy teams.
Typical stack
- Source control: GitHub / GitLab / Bitbucket
- Authoring: Markdown, MDX, reStructuredText, AsciiDoc
- Static site generator: Docusaurus, MkDocs, Hugo, Sphinx, or Nextra
- Hosting: Netlify, Vercel, GitHub Pages, AWS, Azure
- Search: built-in search, Algolia, or OpenSearch
Why this works
- Version control is native
- Pull requests enable SME review
- Easy CI/CD publishing
- Works well for tutorials and docs
Option B: Headless CMS + Git
Best if non-technical authors need a UI.
Typical stack
- CMS: Contentful, Sanity, Strapi, Prismic
- Git integration or approval workflow inside CMS
- Frontend docs site: Next.js, Astro, Docusaurus, etc.
Why this works
- Easier editing for SMEs
- Better structured content fields
- Still can keep source-controlled builds
Option C: Hybrid
Often the best choice:
- Public docs/tutorials in Git-based docs site
- Editorial planning / intake / approvals in a CMS, Jira, Notion, or Confluence
- Final content still published from Git
3) Set up the content repository structure
Example Git repo structure:
docs/
getting-started/
tutorials/
how-to/
reference/
release-notes/
assets/
templates/
snippets/
For versioned docs:
docs/
versioned_docs/
v1.0/
v1.1/
versioned_sidebars/
docs/
latest/
Or keep branches/tags per product version if your platform supports it.
4) Create authoring standards
To make SME review efficient, define standards early:
Content templates
Create templates for:
- Tutorial
- How-to
- Reference page
- Troubleshooting article
- Release note
Each template should include:
- Purpose / audience
- Prerequisites
- Steps
- Expected result
- Validation
- Related links
- SME review checklist
Style guide
Document:
- Voice and tone
- Terminology
- Formatting rules
- Code block conventions
- Screenshot policy
- Accessibility standards
- Link and cross-reference rules
5) Build version control into the workflow
Use Git as the system of record.
Recommended branching model
A simple model works best:
main= published contentfeature/*= draft workrelease/*= version-specific updates if needed
Pull request workflow
Every doc change should go through:
- Author creates branch
- Draft is opened as PR
- SME reviews for technical accuracy
- Docs editor reviews for clarity/structure
- Merge after approval
- CI publishes to staging or production
Benefits
- Audit trail
- Easy rollback
- Review history
- Parallel work across teams
6) Define SME review cycles
You want SMEs involved without turning them into a bottleneck.
A good review model
Use two-stage review:
Stage 1: Content readiness review
Reviewer checks:
- Is the topic correct?
- Are assumptions valid?
- Are steps technically complete?
- Are edge cases covered?
Stage 2: Final approval
Reviewer checks:
- No outdated info
- Links, examples, commands, and screenshots are correct
- Version compatibility is accurate
Review SLA
Set expectations:
- SME initial review: 2–3 business days
- Re-review after revisions: 1–2 business days
- Escalation if no response: after SLA deadline
Rotate SMEs
Avoid one-person bottlenecks by assigning:
- Primary SME
- Backup SME
- Rotating review queue by product area
Use a review checklist
A checklist makes reviews faster and more consistent:
- Correct product/version?
- Steps validated?
- Commands tested?
- Terminology matches product UI?
- Security/compliance concerns addressed?
- Deprecations noted?
- Links resolved?
- Examples reproducible?
7) Add editorial workflow states
Whether in GitHub Projects, Jira, or a CMS, define status stages like:
- Backlog
- Drafting
- Internal review
- SME review
- Revisions needed
- Approved
- Published
- Deprecated / archived
This gives visibility to authors, reviewers, and managers.
8) Automate validation with CI
Set up checks so poor-quality docs don’t publish.
Useful automated checks
- Markdown linting
- Broken link checking
- Spellcheck / terminology check
- Frontmatter validation
- Code snippet tests
- Build validation
- Accessibility checks
- Style checks for headings and lists
Example CI pipeline
- On PR:
- lint
- link check
- build docs site
- run snippet tests
- On merge to main:
- deploy to staging
- On release tag:
- deploy production
9) Support versioned publishing
If your product has releases, docs should match product versions.
Common patterns
- Latest docs: always current
- Versioned docs: docs for each released product version
- Deprecation notices: warn readers when content is outdated
Good practices
- Keep release notes tied to product release tags
- Freeze docs for old versions
- Show visible version selector in the UI
- Document compatibility matrices
10) Make tutorials reproducible
Tutorials need stronger validation than reference docs.
Best practices for tutorials
- Use realistic but simple scenarios
- Include prerequisites clearly
- Keep each tutorial focused on one outcome
- Test in a clean environment
- Use sample data and code
- Include “what success looks like”
- Add troubleshooting notes
Strong recommendation
Have SMEs or developers actually run the tutorial in a test environment before approval.
11) Set permissions and governance
Define who can do what:
- Authors: create and edit drafts
- Editors: revise structure/style
- SMEs: technical accuracy review
- Publishers: merge/release approval
- Admins: repo/site config
Also define:
- When content becomes “owned”
- What happens on team changes
- How deprecated content is archived
- How urgent fixes bypass normal workflow
12) Add metadata and taxonomy
Good metadata makes docs easier to manage and search.
Useful fields:
- Product
- Version
- Audience
- Topic
- Status
- Owner
- SME
- Last reviewed date
- Next review date
- Deprecation date
This can live in frontmatter for Git-based systems.
13) Include feedback loops
Let users and internal teams report issues.
Add:
- “Was this helpful?” buttons
- Feedback form
- GitHub issue link
- Support ticket integration
- Broken-content reporting process
Then triage feedback:
- Bug in docs
- Bug in product
- Missing content
- Outdated version
- Unclear explanation
14) Example end-to-end workflow
A simple workflow might look like this:
- Product team releases a feature
- Docs author creates a documentation task
- Author writes tutorial in a feature branch
- CI validates links/format
- PR sent to SME
- SME reviews and comments
- Author revises
- Editor checks clarity and structure
- PR approved and merged
- Docs site auto-deploys to staging
- Final signoff
- Production publish
- Add review date for future maintenance
15) Suggested starter stack
If you want a practical, low-friction setup:
- Docs framework: Docusaurus or MkDocs
- Version control: GitHub
- Review workflow: Pull requests + GitHub CODEOWNERS
- Task tracking: Jira or GitHub Projects
- Hosting: Netlify or GitHub Pages
- Search: built-in search initially, Algolia later
- Feedback: GitHub issues or a form
- CI: GitHub Actions
This is one of the easiest ways to get strong version control and SME review without overengineering.
16) Minimal operating rules
If you want the shortest possible governance model, use these rules:
- All content changes go through Git PRs
- All tutorial changes require SME approval
- Every doc page has an owner and review date
- CI must pass before publish
- Versioned content is never edited silently; use branches/tags or version folders
- Deprecated docs must be marked clearly
- Feedback is triaged weekly
If you want, I can also provide:
- a reference architecture diagram,
- a sample GitHub workflow with CODEOWNERS, or
- a documentation governance policy template.
Observation history
Unlock observation history
Create a free Obsurfable account to unlock historical responses, model comparisons, and deeper brand intelligence.
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, and Meta AI.