Why CI/CD Still Doesn't Include Continuous Documentation?

In my 15+ years as a developer, one of the most persistent headaches I’ve seen across teams is outdated documentation. I’ll admit it, I’ve shipped features and moved on without updating the docs. A month later, a new teammate is onboarding or someone is debugging an issue, and they run into a README or guide that no longer reflects reality. It’s frustrating for them and embarrassing for us. I’m certainly not alone in this habit. Maintaining documentation is often the last thing on a developer’s mind during a busy sprint. The result is predictable: confused developers, frustrated users, and wasted time chasing down answers that the docs should have provided. We live in a world of CI/CD where code is built, tested, and deployed automatically at high velocity. Yet documentation tends to lag far behind. I’ve seen README files turn into works of fiction as codebases evolve rapidly. It’s not that developers don’t care about accuracy, it’s that keeping docs manually in sync with a fast-moving codebase is nearly impossible. We’ve automated almost everything else in our development workflow, so why hasn’t documentation kept up? In other words, why does CI/CD still not include continuous documentation as a first-class practice? Why Continuous Documentation Isn’t Mainstream (Yet) Let’s break down the main challenges preventing documentation from flowing as continuously as code: DevOps Speed vs. Manual Docs Modern teams push code multiple times a day, sometimes even per hour. Manual documentation updates simply can’t keep pace. By the time you finish writing a how-to guide for a feature, that feature might have changed or been replaced. As one DevOps lead bluntly put it, “manual documentation is physically impossible to maintain when you’re moving at DevOps speed”. In the era of rapid CI/CD, documentation often gets left in the dust. Limited Automation Tools We do have tools that generate documentation from code, but only for narrow slices of the problem. For example, Swagger or OpenAPI can generate API reference docs, and Sphinx or Javadoc can turn code comments into nicely formatted reference manuals. These are helpful, but they don’t cover the higher-level documentation – things like conceptual guides, onboarding tutorials, or README walkthroughs. They also rely on developers keeping those annotations and comments up to date in the first place. In short, existing tools automate some docs (mostly low-level API details) but leave out the broader context and inevitably require manual effort to fill the gaps. Docs Out of the Code Loop A big part of the problem is historical: documentation has often lived outside the codebase. Think wikis, Confluence pages, Google Docs, or Notion. When docs aren’t versioned alongside code, it’s unrealistic to expect them to update in lockstep. Even when teams adopt a “docs-as-code” approach (storing docs in the repository as Markdown), that’s only the first step. Sure, you can set up static site generators like MkDocs or Docusaurus and rebuild docs on every commit – but if the content is stale, an automated publish doesn’t magically fix that. The CI pipeline can deploy documentation changes continuously, but it can’t create those changes on its own. Without a mechanism to detect and correct doc drift, continuous deployment of docs just means continuously deploying outdated information. Developers Skip the Boring Stuff Let’s face it, writing docs is often seen as a chore. In high-pressure environments, people feel documentation slows them down, so it’s neglected as “overhead”. Unlike code or tests, there’s usually no immediate red flag when documentation is wrong – no test fails, no build breaks. So the incentive to update docs in the moment is low. Technical debt builds up in documentation quietly. By the time anyone notices (usually a new hire or an external user finding discrepancies), the original developers have moved on. Culturally, many dev teams have not treated documentation with the same rigor as code, and without an automated safety net, it’s easy for even well-intentioned teams to let docs slide. Fragmented Documentation Needs Another challenge is that documentation isn’t one monolith. A software project has many types of docs – API references, architecture diagrams, setup guides, usage examples, release notes, etc. Each type may require different tools or processes to update. Some information can be pulled from code (function signatures, CLI options), but other knowledge lives only in human heads (design rationale, best-practice guides). This fragmentation makes it hard to have one unified “continuous documentation” process. Teams might automate API docs generation in CI, but still rely on a manual wiki for tutorials, for example. If only part of the documentation is automated, the rest will still fall behind and break the chain of continuity. AI Helps, But Isn’t Perfect In recent years, we’ve tried using AI to ease the documentation burden. I’ve personally experimented with AI IDEs like GitHub Copilot and Cursor that can generate or update docstrings and even suggest README changes. They do help speed up writing, but they come with their own hassles. You have to remember to prompt the AI after every code change, and manually specify what context it should consider. It’s easy to forget, and the AI has no way of knowing which parts of the docs need an update unless you guide it. In a team setting, if each developer runs an AI assistant on their own, you risk inconsistent style and even conflicting documentation changes. One teammate’s AI might rewrite a section in a completely different tone or structure than another’s. We also found that letting an AI regen an entire document can inadvertently mess up the formatting or voice of the docs if you’re not careful. In short, AI-as-you-write is promising, but without integration into the actual code workflow, it’s still a manual, ad-hoc solution – not a continuous one. Lack of Enforcement in Pipelines Unlike code quality (where failing tests or linters will block a merge), documentation rarely has such strict enforcement. Some teams set up link checkers or doc linters in CI, but those only catch broken syntax or links, not whether the content is up to date. There hasn’t been an easy way to automatically detect that “function X changed, but the docs for X didn’t” as part of a CI pipeline. Because of this, even teams who treat docs-as-code and require docs in PRs can’t guarantee the docs remain correct after that PR. Over time, as code diverges, there’s no continuous guardrail catching the drift. Now, the good news is the industry has started recognizing these issues. There’s even a term “DocOps” (Documentation Operations) which essentially means applying DevOps principles to docs – automating and integrating documentation into the development lifecycle. Some forward-thinking teams have built impressive custom setups where any code or infrastructure change triggers automatic doc updates (like regenerating API docs on each deploy, or updating config docs when a Terraform file changes). In an ideal scenario, “Code changes? Docs update. Infrastructure changes? Docs update” – as if a documentation fairy follows the team around fixing things. The benefits of such continuous documentation are huge: no more hunting through outdated pages, faster onboarding, and greater trust in the docs. So if the benefits are clear, why isn’t everyone doing it? Simply put, it’s hard. Until recently, implementing full continuous documentation meant a lot of custom scripting and piecemeal tools. Most teams don’t have the time or resources to build their own doc automation from scratch. We’ve been stuck with either doing it manually or automating only small parts of it. From Frustration to Automation: How DeepDocs Fills the Gap After encountering these pain points for years, we decided enough was enough. If we can automate testing and deployment, we should be able to automate documentation updates too. I wanted something that just works – seamlessly, in the background, without me having to remember anything. That drive led us to build DeepDocs, a GitHub-native AI app for continuous documentation. DeepDocs approaches the problem in a way that closely mirrors CI/CD philosophy. Once you install and configure it on your repository, it becomes an ever-watchful agent in your development workflow. Every time you push changes to your code, DeepDocs is listening. It analyzes the commit diff and your entire codebase to figure out if any part of your documentation might be out-of-sync with the new code. If it finds something, it automatically generates a doc update on a separate branch with the suggested changes. Crucially, these updates are done in a minimal, precise way. DeepDocs doesn’t blindly rewrite your docs or generate pages of text from scratch. It makes surgical edits to address the specific inconsistencies it found, and nothing more. The tool preserves your formatting, tone, and structure – it won’t suddenly reformat your whole Markdown file or change your writing style. It’s smart enough to only adjust the parts of the docs that need updating, leaving the rest untouched. This was a core design goal: developers are rightfully wary of automated tools trampling over carefully crafted documentation. DeepDocs avoids that by respecting the existing docs and just bringing them up to date where needed. Let me give an example: suppose you changed the behavior of a function foo() in your code. The code diff shows that foo now accepts an extra parameter and returns a different data structure. DeepDocs will scan through your doc folder (Markdown files, example scripts, etc.) to find any references to foo() . If it finds, say, an example snippet or an API reference in the docs mentioning the old parameters, it will edit that line in the docs to include the new parameter and adjust the described return format. All of these changes are done in a separate branch so that you can review before opening a PR. Further, it also includes a report explaining why (e.g. “updated the example for function foo() to match the new signature from commit abc123”). You, as a developer, just review this like any other code change. If it looks good, you merge it, and boom – your documentation is now in sync with your code. If something’s off, you can tweak the text or reject the change. I designed DeepDocs to fit naturally into software teams’ existing workflows. It’s GitHub-native, meaning you don’t have to adopt a new platform or host your docs elsewhere. It works with your Git repos directly. You can treat documentation updates exactly like code updates. This aligns with the “docs as code” philosophy, but now the updates to docs are also generated through automation. DeepDocs works with monorepos and even external documentation repositories (for teams that keep code and docs in separate repos). It also plays nicely with popular documentation toolchains. For example, if you have a Docusaurus or MkDocs site, DeepDocs will update the Markdown/source files, and your existing CI job can then rebuild the docs site as usual. One thing to clarify: DeepDocs is not about generating brand new documentation from nothing. It’s about maintaining documentation continuously. You still need to write the initial docs for a feature or module (or at least have decent doc comments). In fact, if you’re starting a project with zero docs, tools like Copilot or other AI writers can help you draft them. But once those docs exist, that’s where DeepDocs takes over the heavy lifting of maintenance. It ensures that those docs don’t decay over time as the code changes. Think of it as an automated editor that knows your codebase and watches for any divergence between the code and the docs. Why DeepDocs Is Different from Other Approaches It’s worth highlighting what makes our approach DeepDocs distinct from previous attempts to solve the documentation problem: Truly Continuous : Unlike manual AI prompting or periodic doc sprints, this runs continuously in the background. Every commit is checked. It’s always on, just like CI tests are always run on each push. This ensures no change slips through unnoticed. As the DeepDocs documentation puts it, docs and code go together, stay together – which is the ideal state we want. : Unlike manual AI prompting or periodic doc sprints, this runs continuously in the background. Every commit is checked. It’s always on, just like CI tests are always run on each push. This ensures no change slips through unnoticed. As the DeepDocs documentation puts it, docs and code go together, stay together – which is the ideal state we want. Full Context Awareness : DeepDocs has access to your full repository context (code + docs). It’s not limited to one file at a time or a small window of text. This means it can understand relationships – for example, if you rename an API endpoint in code, it can find references to that endpoint across all your docs. Traditional code-to-doc generators don’t cross that boundary, and general-purpose AIs only know what you explicitly feed them. By being integrated with the repo, the AI can see the “big picture” diff and act accordingly. : DeepDocs has access to your full repository context (code + docs). It’s not limited to one file at a time or a small window of text. This means it can understand relationships – for example, if you rename an API endpoint in code, it can find references to that endpoint across all your docs. Traditional code-to-doc generators don’t cross that boundary, and general-purpose AIs only know what you explicitly feed them. By being integrated with the repo, the AI can see the “big picture” diff and act accordingly. No New Workflow for Developers : Developers don’t have to learn a new documentation platform or tool. Everything happens via GitHub, via the version control flow they already use. This is important because any solution that requires developers to change their habits drastically is going to face adoption hurdles. DeepDocs was designed to fit into existing workflows (it’s even installed and configured via a simple GitHub App and YAML config). It’s a “lightweight, developer-first approach” to the problem, which lowers the barrier to entry for teams. : Developers don’t have to learn a new documentation platform or tool. Everything happens via GitHub, via the version control flow they already use. This is important because any solution that requires developers to change their habits drastically is going to face adoption hurdles. DeepDocs was designed to existing workflows (it’s even installed and configured via a simple GitHub App and YAML config). It’s a “lightweight, developer-first approach” to the problem, which lowers the barrier to entry for teams. Minimal and Explainable Changes : Every doc update comes as a pull request diff that can be reviewed line by line. There’s no giant black-box re-write of your docs. The transparency here builds trust. The PR description includes a report of what was changed and why. That level of detail means a developer can quickly verify the update or discuss it in the PR if needed. Contrast this with a scenario of an engineer running an AI on their own – the team might not know what context it saw or why it rewrote a paragraph. Having the AI integrated and its actions visible in the repository makes it a collaborative tool rather than a rogue script. : Every doc update comes as a pull request diff that can be reviewed line by line. There’s no giant black-box re-write of your docs. The transparency here builds trust. The PR description includes a report of what was changed and why. That level of detail means a developer can quickly verify the update or discuss it in the PR if needed. Contrast this with a scenario of an engineer running an AI on their own – the team might not know what context it saw or why it rewrote a paragraph. Having the AI integrated and its actions visible in the repository makes it a collaborative tool rather than a rogue script. Focus on Maintenance, Not Generation: As mentioned, DeepDocs isn’t about generating your documentation from nothing (which often leads to generic or bloated docs). It’s about keeping your documentation correct and up-to-date. This focus means it can do a better job of preserving the original voice and intent of the docs. That’s a fundamentally easier problem for AI to get right, and it shows in the quality of updates. Your carefully written tutorials and explanations remain intact, only now they no longer lie to the reader. Of course, continuous documentation is a relatively old concept that has never been put to practice in its truest sense. DeepDocs is one of the first tools, we built it because we couldn’t find an existing solution that met our needs. CI/CD Philosophy for Docs Ultimately, the goal of bringing documentation into the CI/CD fold is about upholding the same philosophy we apply to code: catch issues early, automate repetitive tasks, and deliver value continuously. With code, CI ensures we don’t introduce regressions or break the build without knowing. With documentation, a continuous approach ensures we don’t introduce confusion or misinformation without correcting it immediately. It’s about treating docs as a first-class citizen in the development lifecycle. What I’m most proud of with the continuous documentation approach is that developers don’t have to change their habits to benefit from it. Just write code as usual, commit and merge as usual. The onus of remembering to update docs is lifted off the developer’s shoulders. For software engineering teams, this can be transformative. New hires ramp up faster because the docs actually reflect the current system behavior. Support teams and users find accurate information in the docs, reducing back-and-forth questions. Developers themselves can rely on internal docs when debugging, instead of shrugging them off as hopelessly outdated. Over time, trust in documentation is restored, and when people trust the docs, they are more likely to contribute improvements to them (a positive feedback loop). Continuous documentation also pays off in less obvious ways. Think about compliance and audits in fields like finance or healthcare – with an automated doc trail, showing accurate, timestamped documentation for each release becomes much easier, potentially saving hefty fines or development fire drills. And consider developer productivity: time spent reconciling code with stale docs or updating wiki pages could be spent building new features. By removing a source of toil, engineers can focus on coding, while knowing that an automated process has their back on the documentation front. DeepDocs is just one implementation of this idea, and I expect more tools and services will emerge in this space (some already are). The important thing is that the conversation has shifted from “ugh, documentation is always outdated” to “how can we make documentation continuous and automated?”. We’re finally seeing documentation treated with the same seriousness as testing and deployment in the DevOps cycle. To answer the question posed at the start: CI/CD historically didn’t include continuous documentation because we lacked the right tools and mindset. We treated documentation as something separate from the code, something that required human effort and could be postponed. We now have the technology to close that loop. That’s why we built DeepDocs to solve this in our own workflow, and it’s been incredibly rewarding to see up-to-date docs becoming the norm rather than the exception. I truly believe continuous documentation will become a standard practice in the industry. We’ll look back on the days of stale READMEs and say, “Remember when we used to manually update docs (and usually forgot)? Thank goodness we don’t live in that world anymore.” Code and Docs are meant to stay together, forever