AI Can Explain Your Code. Stop Documenting the Obvious

Documentation has always had a reputation problem.

We ask it to explain complex, evolving systems, then act surprised when it falls out of date. We tell ourselves that this time we’ll keep it current — that better discipline, better tooling, or better templates will fix it.

But the failure mode hasn’t really changed in decades.

The uncomfortable truth is that static documentation was never well-suited to describing living systems.

Code changes daily. Teams change faster. Context disappears quietly. And static files — no matter how well written — slowly lose their authority. They stop being trusted, and once that happens, they stop being read.

This is the classic documentation problem. But in 2026, something fundamental has shifted.

AI Can Explain How. That Was Never the Hard Part.

For years, documentation tried to do two jobs at once:

• Explain how the system works
• Explain why it works that way

The first job is no longer the bottleneck.

With AI, you can explore a codebase directly. You can ask it to explain a module, trace a request, describe a component, or summarise a flow. In language that matches your level of experience and the task you’re working on.

If documentation exists only to explain what the code already makes obvious, then AI has quietly made most of it redundant.

Not because that documentation was bad, but because it was solving a problem that no longer exists in the same way.

Understanding how something works is now cheap.

The More Interesting Question Was Always Why

Take a simple example.

On a news website, the advertising component sticks to the top of the browser for a fixed number of seconds.

AI can tell me:

• Where that behaviour is implemented
• How the timing works
• Which CSS makes it sticky
• What happens when the timer expires

But none of that answers the interesting question.

Why does it stick at all?

That “why” might live in:

• A past revenue experiment
• An editorial constraint
• A legal requirement
• A performance trade-off
• A previous failure we don’t want to repeat

The “how” was always obvious to the computer. The “why” was only ever obvious to the people who were there.

And without that context, future changes are blind. The system becomes fragile not because it’s complex, but because its intent has been forgotten.

Documentation as a Personal Act of Understanding

This is where the role of documentation starts to flip.

Instead of producing exhaustive documentation for everyone, developers can now create personal documentation as they build their own mental model of the system.

Using AI, you can:

• Explore the code
• Ask clarifying questions
• Generate explanations
• Rewrite them in your own words

That last step matters more than it sounds.

A mental model only really forms once you summarise, rephrase, and decide what you think is important. This kind of documentation is incomplete, opinionated, and task-driven — and that’s precisely why it works.

It doesn’t aim to be a permanent artefact. It aims to be a scaffold for understanding.

In many cases, this replaces the old idea of exhaustive documentation entirely.

Making Personal Documentation Practical

Personal documentation doesn’t need to start from a blank page.

In practice, it often works best as a collaboration.

Tools like Codex, Copilot, or Claude Code can help you explore a system and turn that exploration into something more durable. As you investigate the codebase, you can ask them to summarise what you’ve learned, rewrite explanations in your own words, or capture the key ideas you don’t want to forget.

The output doesn’t need to be perfect. It just needs to reflect your mental model at that moment in time.

A simple pattern looks like this:

• Explore the code with AI
• Ask it to explain what you’re seeing
• Ask why certain decisions might exist
• Capture the resulting understanding in a short markdown file

Saved locally, these notes become a personal trail of understanding:

• How you made sense of the system
• What confused you initially
• What assumptions you validated
• What questions still felt unresolved

They’re not meant to be exhaustive, polished, or permanent. They’re working notes — written to help you think, not to satisfy an audience.

Sometimes they’ll be thrown away. Sometimes they’ll evolve. Occasionally, the useful parts will be shared. And that’s fine.

Why Markdown Works Well Here

Keeping this documentation in simple markdown matters more than it sounds.

Markdown:

• Is fast to write
• Stays close to the code
• Ages better than most tools
• Can be read by humans and machines

It keeps the bar low. There’s no wiki structure to maintain, no expectation of completeness, and no pressure to keep everything “official”.

Just enough structure to preserve understanding.

What Still Needs to Be Written Down

If AI can explain what the system does and how it works, then shared documentation can focus on what only humans can reliably provide:

• Why certain decisions were made
• Which trade-offs were accepted intentionally
• What direction the system is moving toward
• What principles should guide future changes
• What not to optimise away casually

This kind of documentation doesn’t try to mirror the codebase. It doesn’t compete with AI or with the code itself.

Instead, it acts as a guiding light. It helps people make good decisions when the code inevitably changes.

From Maps to Compasses

Traditional documentation tried to be a map: detailed, precise, and exhaustive.

But maps go out of date quickly.

In 2026, documentation works better as a compass. Something that doesn’t tell you exactly where everything is, but helps you orient yourself and choose a direction.

AI can help us understand the terrain as it exists today. Documentation should help us remember why we chose this path — and where we’re trying to go next.

That feels like a much better division of labour.