Interactive Explanations: A New Pattern for Understanding AI-Generated Code

When AI agents write our code, we face a new kind of technical debt: cognitive debt. Unlike traditional technical debt where we knowingly take shortcuts, cognitive debt accumulates when we lose track of how our own code actually works.

For simple features—like fetching data from a database and returning JSON—this doesn't matter much. We can test the feature, make educated guesses about the implementation, and verify with a quick code review. But when the core of our application becomes a black box, we lose the ability to confidently reason about it. This makes planning new features harder and eventually slows progress just like accumulated technical debt.

How do we pay down cognitive debt?

By improving our understanding of how the code works. One of the most effective techniques I've discovered is building interactive explanations.

Understanding word clouds

This clicked for me while exploring a Rust word cloud generator. Max Woolf had tested LLMs' Rust abilities with the prompt: "Create a Rust app that can create 'word cloud' data visualizations given a long input text."

I was curious about how word clouds actually work, so I ran my own async research project. Claude Code for web built me a Rust CLI tool that produced beautiful word cloud images. But when I asked how it worked, the answer was "Archimedean spiral placement with per-word random angular offset for natural-looking layouts"—which didn't help me much!

I requested a linear walkthrough of the codebase, which helped me understand the Rust structure better. But I still couldn't intuitively grasp how the spiral placement algorithm actually worked.

So I asked for an animated explanation. I provided a link to the existing walkthrough.md document and requested an animation that would visualize the algorithm in action.

Using Claude Opus 4.6, I got this result:

The animation shows that for each word, the algorithm attempts to place it somewhere on the page by showing a box, then checks if that box intersects with existing words. If there's an intersection, it continues searching, moving outward in a spiral from the center until it finds a valid spot.

This animation made the algorithm click for me in a way that static code never could.

The power of interactive explanations

I've long been a fan of animations and interactive interfaces for explaining concepts. What's powerful here is that a good coding agent can produce these on demand to help explain code—whether it's the agent's own code or code written by others.

This is part of a broader pattern I'm calling "agentic engineering patterns"—ways of working with AI agents that help us maintain understanding and control as they take on more of our coding work.

Interactive explanations are particularly valuable because they transform abstract algorithms into something you can see and understand intuitively. They're not just documentation; they're tools for building mental models.

The next time you're working with code you don't fully understand—whether written by an AI or by a colleague—consider asking for an interactive explanation. It might be the fastest way to pay down that cognitive debt.