The article also recommends making codebases more AI-friendly by breaking systems down into cohesive and coherent modules, each interacting through an explicit interface. This approach allows teams to take full advantage of generative AI tools, leading to rapid feature development and higher quality software. The article concludes by emphasizing that a high-quality codebase is now more valuable than ever due to the benefits it offers in terms of speed and efficiency when using AI tools.
Key takeaways:
- AI has increased the cost of carrying tech debt and widened the gap in velocity between 'low-debt' and 'high-debt' coding, with companies having high-quality codebases benefiting the most from generative AI tools.
- Generative AI tools struggle with high complexity and 'high-debt' environments, leading most developers to wait for the tools to improve until they can handle 'production-level' complexity in software.
- Instead of forcing genAI tools to tackle complex issues in legacy codebases, human experts should refactor legacy code until genAI can operate on it smoothly, implying a shift to a higher level of abstraction for a modern development team.
- Investment into modular architecture allows teams to fully utilize generative AI tools, leading to rapid feature development and higher quality software, making it more valuable than ever to have a high-quality codebase.