# In the Age of AI, Humans Should Commit More to Internal Documentation > Source: > Published: 2026-07-06 21:01:01+00:00 In all my years of working across various organizations, I have never encountered one with a truly well-maintained internal documentation library. Whether it’s decisions made verbally that never got written down, fragmented meeting notes that tell only half the story, or documents that exist but haven’t been updated in so long they no longer reflect reality — at least one of these always applies. Asking a senior team member can sometimes solve the problem, but in the worst cases, you end up with only a legacy business process or system that nobody can explain or change. All the information current employees need was lost somewhere along the way. I suspect many of you have experienced the frustration of thinking, “If only past decisions had been documented, this task would go so much smoother.” In this article, I want to explore what generative AI has made possible in the realm of documentation — what we can delegate to AI, and what humans still need to own. Everyone agrees that better internal documentation would be nice — so why does it keep falling to the bottom of the backlog? Companies do invest meaningful resources in customer-facing documentation. Organizations with SaaS products often hire dedicated technical writers. The reason is straightforward: if external docs are incomplete or outdated, customer satisfaction suffers directly. The business case is obvious, so the resources follow. Internal documentation lacks that same obvious urgency. And the practitioners themselves have their own reasons to ignore it: For all these reasons, achieving anything close to thorough internal documentation has historically been unrealistic unless your organization communicates entirely in async text. But the rise of generative AI is starting to change that situation dramatically. **Note: Intended audience** This article is written primarily for engineers and project managers inside product development organizations whose stakeholders are largely internal. Creating polished external or client-facing documentation is not their primary job. **Note: Scope of “internal documentation”** For this article, internal documentation includes: AI has introduced a kind of leverage that didn’t exist before. Even a team of one to three people can now work as if it had several times the headcount, by pairing with AI that draws on well-maintained documentation. To make this concrete, imagine each person on your team has access to a dedicated support crew: The catch is that this AI support crew isn’t sitting next to you. It can only communicate through text. Even if every human on your team is in the same office, the AI is always working remotely from somewhere else. The COVID-era shift to full remote forced organizations to rethink how they communicate. AI is triggering a similar reckoning: how do we structure our communication so that AI can work alongside us effectively? The old rule of thumb was that small teams that don’t have external stakeholders could get away with minimal documentation — just enough to remind yourself six months later, like a personal time capsule. A README with the key commands or a comment on the code explaining why a particular function is required. As teams grow, the need for documentation inevitably rises. Code ownership gets distributed, other teams start asking questions, and new members join regularly. That’s usually when organizations start seriously thinking about onboarding guides and process documentation. But now that AI is actively participating in development, this inflection point has moved significantly earlier. The return on early documentation investment is higher than ever. The core value of documentation has always been *leveraging asynchronous communication*. One person writes it; a hundred or a thousand people can reference it. That scaling effect is documentation’s greatest strength. AI multiplies this effect dramatically. Now, in addition to those thousand human readers, potentially tens of thousands of AI agents access the same documentation and act on it. The audience for your internal documents is no longer only human — and that is the defining shift that should reshape how we think about documentation strategy in the AI era. We should be aware that at this moment, AI cannot solve all the existing documentation problems by itself. The same lesson from the earlier data science buzz applies here: for AI to perform at its best, it needs well-organized, high-quality information. AI integrations with collaboration tools are expanding. Slack, Notion, and similar platforms now offer AI that can search across them. But simply “connecting” AI to these tools won’t unlock its potential. Collaboration tools accumulate enormous amounts of information that loses its value over time. Old meeting notes for features that no longer exist, unrelated chat threads, stale decisions — all of this inflates the token count when the AI tries to retrieve relevant context. The more noise in the environment, the harder it is for the AI to find what it needs, and the more expensive each query becomes. If humans struggle to find information in a given environment, AI will struggle too. This is why it makes sense to maintain a dedicated documentation environment — a Git repository, for instance — that is separate from your live collaboration tools. The ideal is a place where only valuable, current documents live, organized for easy retrieval. Collaboration tools are excellent for managing ongoing, high-velocity communication like project management and daily collaboration. But because they generate massive volumes of information every day, they are not well-suited as long-term archives for structured knowledge. Scrutiny over AI usage costs is intensifying. Model vendors are moving toward consumption-based pricing, and the era of unlimited access to top-tier models is ending. Even large companies like Uber have made headlines for AI spending that blew through its budget ahead of schedule: Under pay-per-token pricing, the cost-performance ratio of every AI operation matters. Designs that always use the highest-tier models and consume the most tokens will quickly become unsustainable once the current AI hype cycle normalizes. Because you will be required to get the most out of a fixed AI budget quite soon, you need to enable AI to find relevant information with cheaper models using fewer tokens by optimizing the documentation environment itself for AI consumption. So what does it actually look like for humans to set up an environment where AI can make effective use of documentation? Many of the documentation best practices that predate generative AI are still valuable for AI-assisted workflows. *Software Engineering at Google* recommends keeping important documentation under the same source control used to manage code: The way to improve the situation was to move important documentation under the same sort of source control that was being used to track code changes. Documents began to have their own owners, canonical locations within the source tree, and processes for identifying bugs and fixing them; the documentation began to dramatically improve. Additionally, the way documentation was written and maintained began to look the same as how code was written and maintained. Errors in the documents could be reported within our bug tracking software. Changes to the documents could be handled using the existing code review process. Eventually, engineers began to fix the documents themselves or send changes to technical writers (who were often the owners). [Software Engineering at Google](https://abseil.io/resources/swe-book/html/ch10.html#documentation_is_like_code) The implication is clear: documentation isn’t supplementary material — it deserves the same quality management as code. GitLab, famous for being a fully remote company with no offices, publishes much of its internal documentation publicly on the web. The depth and quality of its handbook are genuinely one of the best in the industry: Reaching that level of completeness isn’t realistic for most teams, but it represents one vision of what well-managed documentation can look like. AI slop refers to the low-quality, redundant content that AI generates at scale. Now that AI can assist with writing documentation, there is a real risk that unreviewed AI output gets merged in — containing inaccuracies, or just being uselessly long. GitLab addresses this explicitly in its communication guidelines, which permit AI as a support tool while setting clear guardrails against slop: When AI is writing your docs, it is tempting to annotate a piece of content with “AI says this” and move on without verifying the content. But as the guidelines make clear, the ultimate responsibility for accuracy rests with the human. Incorrect information in documentation propagates. Validating content is — and will increasingly be — a distinctly human responsibility. Helping AI understand the tacit knowledge held by senior team members is one of the most impactful documentation investments your team can make. Some concrete approaches: **Agent Skills for knowledge encoding** Development conventions, debugging procedures, and other accumulated know-how that lives in people’s heads can be encoded into Agent Skills — documents that AI agents pull in on demand, rather than loading all at once. Skills can be distributed from a central organization-wide repository to individual project repositories, enabling consistent knowledge reuse across teams. [About agent skills - GitHub Docs](https://docs.github.com/en/copilot/concepts/agents/about-agent-skills) **ADRs, design documents, and specifications** Tooling like OpenSpec allows the decisions and changes that emerge during a product development cycle to be systematically documented alongside code. [GitHub - Fission-AI/OpenSpec: Spec-driven development (SDD) for AI coding assistants.](https://github.com/Fission-AI/OpenSpec/) However, full specification-driven development requires documenting every significant change, which can be burdensome for smaller applications. Starting with just the initial system design and key ADRs may be more sustainable. The priority is keeping documentation current and accurate — finding the right balance between operational overhead and documentation richness is more important than any particular format. **CI/CD as guardrails** AI behaves probabilistically — the same input doesn’t always produce the same output. And even if ADRs live in the same repository, there’s no guarantee that AI or humans will notice when a change conflicts with an existing decision. For the decisions that most need to be respected, the most reliable enforcement mechanism is automating them as code. Policy as Code tools like [Sentinel](https://www.hashicorp.com/en/sentinel) and [Open Policy Agent](https://www.openpolicyagent.org/) let you encode rules — always make storage private, restrict expensive instance types — that were previously enforced only by human review. With these in place, violations surface as clear errors before anything reaches production. Generative AI has fundamentally changed the landscape around internal documentation. The cost of creating documentation has fallen sharply, and the pool of “readers” — including AI agents — has grown dramatically. Documentation has become a more powerful lever for accelerating software development productivity than ever before. But this change has a flip side. Organizations that have not invested in managing their documentation will find that AI-powered automation does not work as efficiently as expected. In other words, the quality of your documentation is increasingly synonymous with your organization’s productivity in the AI era. There is an interesting workplace dynamic at play here as well. The return-to-office debate has often centered on the value of spontaneous in-person communication and mentoring junior colleagues. Meanwhile, practitioners may find that having a well-informed AI agent at their side is more productive than ad hoc desk conversations. Organizations that rely heavily on synchronous, in-person knowledge-sharing naturally deprioritize documentation — and as a result, are less positioned to benefit from AI agents. Hybrid and fully remote organizations, which have already built async-first communication cultures, tend to be better aligned with the way AI works. Documentation work has always been unglamorous. Nobody thanks you for keeping it up to date, and nobody writes a viral post about the breakthrough they achieved by maintaining their ADR library. But it is an indispensable foundation for keeping AI costs manageable and driving sustained productivity gains over the long term. Precisely because we are in the AI era, humans need to take documentation more seriously than ever. [In the Age of AI, Humans Should Commit More to Internal Documentation](https://blog.devgenius.io/in-the-age-of-ai-humans-should-commit-more-to-internal-documentation-d4018e3b4d75) was originally published in [Dev Genius](https://blog.devgenius.io) on Medium, where people are continuing the conversation by highlighting and responding to this story.