10 Steps to get your codebase ready for AI

Everyone knows that AI is set to disrupt the industry, but what steps can a developer take to actually get ready for it? AI is not a magical hat where you can tell it to "make me a video game" and it'll spit out a AAA video game, but it's already quite competent at generating usable code.

To take advantage of this powerful tool, here are the real steps you can take with your own codebase, to help make it easier for AI to help you:

1. Standardize Coding Practices:
   • Implement consistent coding standards across your codebase. This makes it easier for AI tools to understand and modify code without introducing errors.
   • Use well-documented and widely adopted coding conventions and styles to reduce complexity.
2. Improve Code Documentation:
   • Ensure that all parts of your codebase are well-documented. This includes comprehensive inline comments, clear docstrings for every function and module, and up-to-date README files.
   • Document not just what your code does, but also why certain decisions were made. This contextual information can be crucial for AI understanding and reasoning, because at its core, AI is a language engine.
3. Refactor and Modularize Code:
   • Break down large, complex systems into smaller, more manageable modules. Modular code is easier for AI to analyze and work with because it can focus on smaller segments of logic. It also helps establish the architecture to make it easy to plug and play AI generated code.
   • Ensure that each module or component has a single responsibility and minimal dependencies on other parts of the codebase.
4. Establish Clear Interfaces:
   • Define clear and consistent interfaces for different components of your system. This includes using abstract classes and interfaces in object-oriented programming languages.
   • Well-defined APIs with documented expected inputs and outputs make it easier for AI tools to understand how different parts of your system interact.
5. Adopt Type Annotations:
   • Use type hints in languages that support them (like Python, TypeScript). Type annotations make the code more descriptive and easier to understand, which can help AI tools in type checking and in predicting the types of variables and return values.
6. Reduce reliance on external libraries:
   • This runs counters to years of practice ("Don't reinvent the wheel!"), but the fewer external libraries, such as npm packages, that you use in your codebase, the easier it will be for AI to understand what the code is doing
7. Leverage Code Linters and Formatters:
   • Use tools like linters and formatters to enforce coding standards and style guidelines automatically. Consistency in coding style reduces cognitive load and makes automated code generation more reliable.
8. Ensure Data Accessibility:
   • Make sure that data used by your application is accessible through well-defined functions or APIs. AI can use these access points to generate data-driven logic and integrate it seamlessly into your application.
9. Ensure Output Accessibility:
   • Using a web applications as an example, the more accessible your HTML is, the easier of a time AI will have consuming it in order to match the output. This is because the closer your code is to language and description (all things that are important in ADA compliance), the more information AI has to work with.
10. Prepare for AI Integration:
    • Consider including hooks and integration points for AI tools within your codebase. For example, setting up placeholders for AI-generated code or making your functions and methods more general to accommodate AI enhancements.