LLMs Still Write Inefficient Database Code

TL;DR. A developer discusses persistent issues with Large Language Models generating suboptimal database queries, even from advanced models like Fable 5. The problem stems from LLM-written tests failing to detect performance bottlenecks rather than functional errors. This leads to code that works on small datasets but fails with larger, real-world data, highlighting a critical gap in AI code generation.

• Tests generated by LLMs often validate functionality but not code efficiency or performance.
• Even advanced LLMs continue to produce inefficient database read operations.
• The issue persists despite specific instructions given to AI models.
• Suboptimal AI-generated code can lead to significant problems with larger datasets.

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