How to make your codebase AI friendly?

AI-assisted coding models are trained on vast codebase datasets to make context-aware predictions for code completion, refactoring and debugging. This training enables LLMs to learn the syntax, semantics, and programming patterns that, when combined with the context of your codebase, help them make code completion and generation as accurate as possible.

Knowing this, it is not surprising that codebases that provide a great developer experience and are beginner-friendly will benefit the most from AI-assisted coding.

Here are the key things to consider to make your codebase friendly to both developers and AI coding assistants.

1. Code structure and readability

You should start with the basics with a well-structured codebase organized into well-defined modules and components with simple purposes. It should be opinionated and clear on how to add or extend a feature so there should be well-defined and documented golden paths to perform specific actions.

2. Clear, concise and contextual documentation

Ideally, these range from inline comments to READMEs and documentation sites that make new contributors productive faster and provide critical context for AI models to make high-quality contributions.

3. Consistent coding standards

Your codebase should have strong, opinionated conventions for code styles, ideally already codified using tools like Prettier or editorconfig. You should have clear and meaningful naming conventions, as well as well-defined error handling strategies.

4. Automated built-in guardrails

This is the most critical part: it should be extremely difficult to produce code that breaks your conventions or introduces bugs that are easily avoided. You should have static checks in place, such as linting with tools like ESLint. Strongly typed languages have a big advantage here, with well-defined interfaces that prevent incorrect code from accidentally creeping in. For this reason, coding assistants will perform much better in a TypeScript codebase than in a Javascript one.

5. Automated code quality checks

Once the code is generated and your codebase is built, you also want to make sure that deploying it does not bring down your entire application and that it works as expected. This is where a strong testing culture pays off, with good unit tests (which your AI assistants can also help you create) as well as well-written end-to-end tests that cover your core customer journey. As part of our continuous integration and continuous delivery system, they will ensure that your baseline is not degraded and that your codebase and feature set grow sustainably.

6. Good observability

Once deployed to production, you should be able to receive quality signals when your customer experience is not working as expected with monitoring and observability best practices. This way, even if a mistake creeped in, you should be able to catch it and correct it early.

If you have made it this far, you will tell me “But Jeremy, these are just software development best practices” and you would be 100% right, and that is the best news of all: making your codebase AI-friendly is as simple as making your codebase beginner and customer friendly. So if you are not already following all of these best practices, you should be, as you will reap immediate benefits and make your codebase better for potential future AI-generated contributions.