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deepmind.google·6d ago
AI Engineering
Hacker News·25d ago·5 min read
Write code like a human will maintain it
TL;DR
Relying on AI to generate code can lead to poor maintenance practices, as developers may overlook best practices like DRY (Don't Repeat Yourself). In a recent project, repetitive access checks were generated by an LLM without considering cleaner solutions like shared helpers.
✦ Why It Matters
Developers should prioritize code maintainability by extracting shared logic into helper functions, even when using AI-generated code.
Key Takeaways
- Always extract shared logic into helper functions to improve maintainability.
- Avoid relying solely on AI for code generation; review and optimize generated code.
- Be mindful of coding patterns you establish in your codebase.
- Regularly refactor code to adhere to best practices, even when using AI tools.
Full Summary
Large Language Models (LLMs) can generate code quickly, but this convenience can lead to laziness in following best practices. In a recent project, the author repeatedly used an LLM to create similar access checks across multiple components, resulting in redundant code with slight variations.
Instead of extracting shared logic into a helper function, the author accepted the generated code as sufficient because it worked and passed tests. However, this approach risks creating a codebase that the LLM will continue to replicate without optimization.
Each shortcut taken sends a signal to the LLM about coding standards, leading to further redundancy. The findings highlight the importance of maintaining coding standards even when using AI tools, as they can influence future code quality.