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The day I taught AI to read code like a Senior Developer

Jan 05, 2025 - nmn.gl
The article discusses a shift in AI code analysis from a linear, file-by-file approach to a more context-aware method that mirrors how senior developers review code. By grouping files based on related functionality and providing context before analysis, the AI can achieve a deeper understanding of the codebase, akin to a senior developer's perspective. This approach has led to significant improvements in the AI's ability to identify complex issues, such as potential race conditions and architectural impacts, without relying on more advanced machine learning models.

The key to this improvement lies in prioritizing system understanding, pattern matching, impact analysis, and historical context, which allows the AI to make more informed suggestions and catch issues beyond its initial programming. This method highlights the importance of code understanding over mere code generation, suggesting that the future of AI in development lies in teaching it to think like experienced developers, potentially identifying tech debt, suggesting architectural improvements, and understanding team conventions.

Key takeaways:

  • AI code analysis improved by mimicking senior developer strategies, focusing on context and system understanding.
  • Grouping files by functionality and size allows AI to perform more effective impact analysis and pattern matching.
  • The new approach led to AI achieving a deeper understanding of code, identifying potential issues and improvements.
  • Future goals include teaching AI to recognize tech debt, suggest architectural improvements, and understand team conventions.
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