Dominic Jainy has spent years straddling codebases and content, stitching documentation into the rhythm of delivery the same way we treat tests and builds. His approach treats docs as first-class citizens in the CI/CD flow, using the same branches, gates, and tooling as the code. In this conversation, he unpacks how Docs-as-Code changes behaviors: it shifts priorities so documentation is never an afterthought, tightens collaboration across dev, QA, and UX, and surfaces risks earlier—especially in security and other non-functional areas. We explore how pipelines carry docs from draft to publish, how reviews are structured to keep code and content aligned, why standardization and linting matter at scale, and what it takes to win buy-in while keeping costs in check.
Key themes we’ll cover include integrating documentation into the CI/CD lifecycle from the first commit, orchestrating tooling across version control, hosting, and static site generators, designing review rituals that catch mismatches before release, and enforcing consistency through linters and style rules. We also look at how to organize repositories and permissions, how to connect tests to docs changes, how to reason about total cost of ownership, and how collaboration and security planning make the whole system resilient.
Docs-as-Code creates docs alongside code with the same tool stack. How did you roll this out step by step, and what hiccups did you hit in the first sprint? Share an anecdote and any cycle time or defect metrics that changed.
We started exactly where friction was lowest: the developer workflow. I introduced a shared repository structure where docs lived next to the services they described, and we wrote initial drafts before any coding began. The idea was simple—draft the documentation, develop the code, and then share both together for review—but the cultural shift was the real lift. In the first sprint, a merge stalled because reviewers were unsure whether to treat incomplete docs like failing tests; that tension surfaced in a lively stand‑up where folks admitted they were used to shipping code while promising to “fix docs later.”
The moment that changed minds was a small API flag described in the draft but missing in the initial implementation. Because the draft sat right next to the code, the mismatch was obvious during review, and we resolved it before it reached QA. While I’m avoiding specific numbers, the trend line spoke for itself: cycle time didn’t balloon, and defect discussions moved earlier in the process. The team could feel it; you could almost hear the sigh of relief when QA didn’t have to chase ambiguities spilled over from the code.
You link documentation to releases through CI/CD. Walk me through the exact pipeline stages you use, from draft to publish. What tooling binds them, and what measurable gains did you see in lead time or failed deploys?
Our pipeline treats docs as a first-class artifact. On commit, a CI job runs linters for Markdown and prose, validates internal links, and checks that the docs version matches the code version. Next, static site generation kicks in, building a preview environment tied to the branch so reviewers can see the changes as a real site, not a diff jungle. After review approval in version control, the pipeline promotes the previews to a release candidate, runs continuous testing, and publishes docs alongside the code as part of the same release.
We bind everything with version control hooks and CI/CD jobs—nothing exotic, just the same tool categories we rely on for code: version control, CI/CD pipeline tools, and a static site generator. The experience of approving one release with both code and docs tightened our lead time predictably because we weren’t waiting for a separate documentation train. Failed deploys from missing configuration steps fell off, not because we became perfect overnight, but because the docs clarified steps before they ever reached production.
The process lists: draft docs, develop code, then share for review. How do you structure those reviews so docs and code stay aligned? Describe roles, timing, and any checklists, plus an example when this caught a serious mismatch.
We run paired reviews: one technical reviewer for code and one content reviewer for documentation, with QA and UX invited for features that touch behavior or interface. The timing is synchronized—no code merges without documentation sign‑off, and vice versa. Our checklist covers requirement mapping, configuration completeness, API request/response accuracy, error handling, and compatibility notes, plus “does the doc anticipate non-functional tests,” like performance and security.
Once, the review caught a performance‑sensitive pagination feature where the docs promised stable ordering and explicit limits, but the code defaulted to a different cursor behavior. The mismatch would have led to user confusion and inconsistent test results. Because the checklist made us verify request parameters and their constraints, we caught it early. The fix was calm and quick; the review felt like a conversation rather than a fire drill.
You mention continuous integration, testing, and version control. How do you connect test suites to docs changes in practice? Give a concrete workflow, tools involved, and a story where docs-driven tests found a bug early.
Documentation changes trigger the same validation journey as code. When a doc updates an API parameter or a configuration step, our CI job tags the change and kicks off targeted tests—think of it as selective continuous testing keyed off documentation deltas. The version control commit message carries a marker, and the pipeline uses that to run applicable suites. If the docs touch security topics, we queue non-functional tests in the same pipeline run.
A memorable example: a doc update clarified a default timeout in a service. That small edit triggered the test suite for timeouts, which surfaced a brittle retry loop that didn’t honor the documented default. We fixed the loop before it reached staging. It felt like the documentation itself tapped us on the shoulder and said, “Are you sure this is true?” That’s the behavior we want—docs guiding tests and tests guarding truth.
Markdown is a core format here. How do you keep Markdown consistent across teams at scale? Describe your style rules, linting with MarkdownLint or Vale, and an example metric (PR rework rate, readability scores) that improved.
Consistency starts with a living style guide: heading hierarchy rules, sentence case for titles, code fence conventions, callout patterns for warnings and notes, and standardized front‑matter for versioning and ownership. MarkdownLint enforces structural rules—no stray headings, proper list indentation, clean links—while a prose linter ensures consistent tone, terminology, and inclusive language. We also provide templates for common pages—release notes, API guides, setup instructions—so writers aren’t reinventing the outline.
What changed was the rhythm of reviews. Instead of nitpicking spacing or title formatting, reviewers focused on substance. You could feel the reduction in churn; pull requests arrived cleaner, and discussions centered on accuracy and clarity. Readability improved because our rules favored concise sentences and consistent examples, and contributors grew confident that “good enough” looked the same across teams.
Static site generators like MkDocs, Jekyll, Hugo, and Docusaurus are options. Which did you pick, why, and how did you migrate? Compare build times, plugin ecosystems, and maintenance overhead, with before-and-after numbers.
We chose a static site generator that fit our Markdown-first approach and could scale with a high volume of documentation. The deciding factors were a solid plugin ecosystem for navigation, search, and versioning, plus smooth integration with our CI/CD. Migration started with a proof of concept: we ported a small section, validated link integrity, and mapped our content structure to the generator’s navigation model. From there, we moved folder by folder, keeping the site build green at each step.
Plugin coverage mattered for things like code tabs, diagram support, and cross-references, and maintenance stayed manageable because the generator relied on predictable configuration rather than custom scripts. Build times were steady and reliable in CI. The best part was watching contributors spin up a local preview quickly; it brought a tactile feel to editing, like hearing a page turn as you write.
IDEs such as VSCode and IntelliJ support both code and docs. What extensions, snippets, or templates make writers and engineers faster? Share your setup, onboarding steps, and a story where this cut review churn.
We standardize on IDE extensions for Markdown preview, link checking, linting, and spellchecking. Snippets provide scaffolds for common sections—Overview, Prerequisites, Steps, Verification, and Troubleshooting—so a new doc opens with the right bones. Templates live in the repo and include front‑matter fields for version, owner, and related tickets, and we wire tasks so the IDE can run a local static site preview with a single command. During onboarding, we pair a first‑time contributor with a doc maintainer to co-author a small change, then ship it end to end through CI/CD.
One story sticks with me: a backend engineer wrote a new integration guide using the template and snippets. Reviewers didn’t have to ask for a prerequisites list or examples—the structure coaxed those details out of the author. The pull request landed with minimal churn, and the engineer said it felt like following a well-marked trail, not hacking through a thicket of expectations.
Version control options include Git, CodeCommit, Mercurial, and Helix Core. How did you decide, and what branching and review model keeps docs releasable? Explain your merge gates, and cite metrics like mean review duration or rollback frequency.
We aligned with the most common tool in our SDLC so contributors didn’t face a second learning curve. The branching model mirrors code: feature branches for changes, short-lived releases, and protected main. Merge gates require green CI on docs and code, approval from technical and content reviewers, and passing checks on links, images, and version headers. We also protect branches with required reviews and status checks so no one accidentally bypasses the process.
The result is a dependable cadence where docs remain releasable at most points in time. Reviews move steadily because they’re scoped with templates and checklists, and rollbacks are rare since ambiguity is flushed out in the branch previews. The process feels lightweight because it’s consistent; contributors know exactly which lights need to turn green.
Hosting on GitHub, GitLab, or Bitbucket is common. How do you organize repos, permissions, and environments for docs? Walk through your folder structure, branch protection, and a lesson learned from an access or merge mishap.
We keep docs inside the service repos when content is tightly coupled to code, and we use a central docs repo for cross-cutting guides. Folder structure follows a predictable pattern: getting-started, concepts, procedures, reference, and release-notes, with assets separated from Markdown. Environments include branch previews, a staging site tied to release candidates, and production tied to tagged releases. Permissions use role-based access: contributors can open pull requests, maintainers review and merge, and admins manage settings and secrets.
We learned a hard lesson when a well-meaning contributor merged a doc fix directly to a protected branch from the web UI. The change was harmless, but it skipped the link checker and broke a sidebar. We tightened branch protection to require status checks regardless of merge path and added guidance in the contributor docs. No blame—just better guardrails.
CI/CD tools like GitHub Actions, Jenkins, and GitLab CI/CD can build and test docs. What jobs do you run on every commit versus nightly? Share sample workflows, cache tricks, and concrete build time or flakiness improvements.
On every commit, we run fast jobs: lint Markdown, check links, build the static site, and run targeted tests tied to the changed areas. Nightly, we run deeper scans—full-link validation across external references, accessibility checks, and complete test suites for docs-driven scenarios. Caching the static site generator dependencies and node modules reduces rebuild overhead, and we cache image optimization results keyed by file hash so unchanged assets don’t churn.
This balance keeps feedback loops tight during the day and catches the long-tail issues overnight. Flaky steps disappeared when we isolated external link checks to nightly runs and added retries with backoff. The pipeline feels crisp: the green checks appear quickly, and the nightly job acts like a quiet guardian while we sleep.
For API-heavy apps, Swagger, OpenAPI, or Readme.io help. How do you keep specs, code, and examples in sync? Describe your source of truth, example generation, and a real incident where drift hurt consumers and how you fixed it.
We treat the API specification as the source of truth and keep it under version control next to the service. The docs pull from that spec to generate reference content and examples, so changing the spec triggers updates downstream. Example code snippets are generated from the spec and tested in CI to ensure they run as documented. When we publish, the docs and the spec ship together as part of the same release.
Once, drift crept in when someone updated handler logic without updating the spec. Consumers hit an endpoint with parameters the doc said were valid, and requests didn’t behave as described. We fixed it by restoring the spec as the single touchpoint: change the spec, regenerate docs and clients, then update the code to match. That incident cemented the practice of drafting docs first and letting the spec drive behavior.
Tools like SWIMM or Doxygen can orchestrate docs. Where do they fit in your stack, and what gaps do they close? Give a step-by-step usage example and a metric like onboarding time or knowledge coverage that moved.
These tools complement our core stack by knitting code and context. We use them to map code flows, annotate critical paths, and connect implementation details to the conceptual docs. The flow is straightforward: add annotations in code, generate contextual pages, link them into the reference section, and surface them in reviews. It’s especially helpful for onboarding and for complex modules where behavior spans multiple files.
The impact shows up when a new engineer traces a feature: they can hop from a concept page to annotated code and back to the procedural guide without losing their place. Knowledge coverage improved simply because those connections made it easy to fill gaps—when an annotation points to a missing explanation, someone adds it. It feels like turning on lights in a dim hallway.
You cite better quality and earlier bug finds, especially in security and non-functional tests. Tell a story where docs highlighted a risk early. What artifacts flagged it, what tests changed, and what cost or time did you save?
A security section in a draft called out permission boundaries for a new admin feature. Seeing it in plain language made us realize we hadn’t specified rate limits or failure responses for unauthorized access. That note triggered non-functional testing focused on security, and we added scenarios for brute-force attempts and privilege escalation. The documentation’s clarity pulled the risk into the open before code hardened around a flawed assumption.
The relief was palpable: instead of scrambling after a penetration test, we adjusted requirements, added guards, and documented clear error messages and monitoring hooks. The cost we avoided wasn’t just rework—it was the stress of correcting a security gap late in the cycle. The docs served as a mirror we couldn’t ignore.
You stress standardization, consistency, and maintenance. How do you schedule doc updates so they’re never “later”? Explain your SLA, tracking in tickets, and a dashboard or KPI you use, with a concrete trend line.
We include documentation tasks in the same tickets as the code and attach a simple SLno feature is “Done” until its docs are drafted, reviewed, and published through the pipeline. The board shows documentation status as a distinct checklist, so it’s visible during stand‑ups and reviews. A dashboard tracks open doc tasks per sprint, review age, and broken link counts; if any rises, we swarm and resolve them before release.
Over time, the line for “docs pending” shrank as teams got used to the rhythm. It wasn’t magic—just consistent habits. The visibility made it impossible to defer documentation without an explicit conversation, which changed behavior more than any policy could.
Buying choices include install, integration, training, and SaaS upgrade costs. How did you model total cost of ownership for your stack? Share your worksheet inputs, vendor surprises, and one place you intentionally paid more to save time.
Our worksheet lists tool licenses, install and integration efforts, training time, and the ongoing maintenance and upgrade cadence. We factor in change management: if a tool matches existing SDLC habits, adoption friction drops, and that translates directly into lower cost. Vendor surprises often hide in upgrade policies for SaaS—feature gates tied to tiers or throttling on build minutes—so we model peaks as well as steady-state usage. Documentation hosting and CI/CD both get special scrutiny because they touch every commit.
We chose to pay more for an integrated CI/CD package that handled both code and docs elegantly. The premium saved us from juggling disparate systems and reduced the chance of pipeline drift. That decision traded a visible line item for a quieter team day to day, which is a bargain I’ll take.
Best practices include team buy-in, security, and collaboration. How did you win support across dev, QA, and UX? Describe your kickoff, security controls in repos and pipelines, and one anecdote where collaboration clearly improved an outcome.
We started with a kickoff that framed Docs-as-Code as a way to make everyone’s job easier: clearer requirements for dev, better coverage for QA, and more predictable experiences for UX and users. Then we backed it up with simple wins—templates, linting, and previews that showed value immediately. Security controls came baked in: branch protections, required reviews, secret-scoped environment variables, and CI/CD permissions that separate build from deploy. By placing guardrails in the platform, we didn’t have to rely on perfect memory.
The collaboration moment I remember best was a UX-led rewrite of a setup guide. The draft language made steps gentler and the error messages more human, which helped QA craft stronger tests and nudged devs to refine messages in code. When we shipped, support tickets dropped, and the team shared a quiet moment of pride. It felt like we all tuned to the same frequency.
Do you have any advice for our readers?
Start small and make the pipeline do the heavy lifting. Put documentation next to the code, agree on a simple checklist, and let CI/CD enforce it without drama. Use the tools you already know where possible; familiarity lowers the barrier and speeds adoption. Most of all, write drafts early—seeing your assumptions on a page is the fastest way to catch what’s missing before it becomes expensive.