Why Documentation Matters More Than Ever in the Age of AI

First, a quick disclaimer: I'm still not convinced that software engineers will be replaced by AI anytime soon—although, honestly, I'm beginning to have my doubts lately.

In my experience, software engineering goes way beyond just coding. There's a lot of thinking, reasoning, and coffee drinking involved — stuff that’s tough to imagine being fully handed off to AI anytime soon. Still, there's no denying we're entering a new reality. AI isn’t just knocking at our door—it's already sitting comfortably in our living room. At this point, you either embrace it or risk falling behind.

The Rapid Rise of AI and the Importance of Context

Large Language Models (LLMs) are getting smarter at a speed that makes your head spin. Every day we see new tools popping up, new concepts getting tested, and honestly, it feels like an invasion of AI in every aspect of our work. Tools like Cursor.ai, GitHub Copilot, and others aren't some distant sci-fi fantasies—they're already reshaping how we work right now.

These AI tools are great at quickly understanding even those ancient "dinosaur" projects that no one wants to touch. But here's the catch: while they're amazing at understanding what your code does, they often have no idea why someone wrote it that way. AI can digest lines of code, but it struggles with the story behind it—internal decisions, reasons discussed during meetings, or simply why somebody thought a particular implementation was a good idea.

Imagine hiring a seasoned software engineer and throwing them into your messy codebase without documentation. They'd spend their days bugging colleagues, trying to piece together the puzzle. Now imagine an AI trying to awkwardly have a coffee chat with a coworker—yeah, doesn't really work, does it?

Architectural Decision Records (ADRs) as AI Enablers

That's where good documentation steps in, particularly Architectural Decision Records (ADRs). If you haven't heard of ADRs, they're basically lean, one-pager documents that explain the "why" behind important tech decisions. ADRs have always been useful—they keep engineers sane and teams aligned without endless re-debates. But today, they're even more crucial. They provide the exact context AI coding assistants desperately need to be actually useful.

By clearly documenting system decisions, you're helping AI make smarter, context-aware recommendations for your internal systems. And let's face it, you're also helping future colleagues figure out what on earth you were thinking when you wrote that questionable piece of code. If writing ADRs manually feels tedious, why not let AI draft the boring parts for you? Ironic, but practical!

(Side note: A similar logic applies to business documentation. High-quality documentation of business decisions, results from A/B tests, KPIs, and similar information can hugely enhance the effectiveness of increasingly smart LLMs. When combined with market research data, internal context could enable AI to actively contribute to innovation and strategic decision-making. Honestly, I'm not sure exactly how this will play out, but I'm pretty confident it will happen soon enough.)

Conclusion: Embracing Documentation in the AI Era

Sure, it feels like total chaos with AI innovations popping up daily. But even in the middle of this whirlwind, some things are clearly sticking around. Agentic coding assistants can read and understand codebases faster than any human developer, but without proper context documentation, they're as clueless as your intern on their first day.

Proper documentation—technical and business—isn't just nice to have anymore. It’s essential, and you might even think of it as a foundational piece of your business's overall data strategy.

For further reading, check out this great article on how AI is reshaping the software development world, and visit the Architectural Decision Records GitHub page for practical guidance on ADRs.