Just about everyone at this point has figured out that LLMs are great at writing greenfield code. The remaining divide is whether they can provide similar help in real production codebases. But here's what nobody wants to admit: if you're having trouble using LLMs in your codebase, most likely it is a problem with your codebase, not the LLM.

If you're expecting Claude or GPT to magically navigate your 3,000-line service files and spaghetti dependencies, you're setting yourself up for disappointment. The uncomfortable truth is that LLMs amplify your existing code quality: good codebases get better, bad ones get worse.

The Real Test

Want to know if your codebase is LLM-ready? Try this: can a new junior developer understand where to add a feature without a two-hour architecture walkthrough? If the answer is no, your AI pair programmer is going to struggle just as much.

The codebases that work well with LLMs aren't special—they're just well-engineered. Here are four key attributes we've identified that are important for engineers and even more important for LLMs.

Clear structure and boundaries. Every component has obvious responsibilities. Business logic lives in one place, not scattered across controllers and utils. When you need to add user authentication, there's exactly one place it should go, and it's obvious where that is.

Small files, clean dependencies. LLMs have context windows, not infinite memory. If your service class imports half the codebase just to save a user, you're going to get suggestions that break three other systems. Keep files focused and dependencies explicit.

Fast feedback loops. Strong types and comprehensive tests aren't just nice-to-haves—they're how you catch AI mistakes before they hit production. Types catch the obvious errors, tests catch the subtle ones. Without both, you're flying blind.

Documentation that explains why. Comments that say // increments counter are useless. Comments that say // batch size must be under 100 or the payment processor times out are helpful. Documents that give full on how-to tutorials for important modules are gold. LLMs need context about constraints and trade-offs and the expected way to use tools, not just what the code does.

The Paradox

Here's the thing: all of these practices are things that dramatically help human engineers as well. We all benefit from knowing where to put things, minimizing cognitive load around changes, having fast feedback loops, and well-documented codebases.

The very practices that make code maintainable by humans are exactly what make LLMs effective collaborators.

Stop Making Excuses

It's easy to fall into the temptation of "vibe coding" and say you'll "clean up the code later" once you have AI helping you. That's backwards. Use the LLM to help you clean up your code, so that you can keep using it to help you. Writing tests, documentation, and refactoring have never been easier than they are with the current tools.

The future isn't about making engineering less important. It's about making engineering discipline even more critical. The teams that will dominate the next decade are the ones that realize AI doesn't replace good engineering practices—it rewards them.