Write code like a human will maintain it (unstack.io)

A recent anecdote highlights a crucial pitfall in using large language models (LLMs) for coding: while they can quickly generate functional code snippets, they may propagate bad coding practices if not used cautiously. The author observed that by allowing an LLM to generate repeated logic for access checks in multiple places—without refactoring or creating shared helpers—they inadvertently created a cycle where the LLM learned and replicated suboptimal patterns, reinforcing poor practices in the codebase. This issue is significant for the AI/ML community because it underscores the importance of maintaining code quality and best practices when integrating AI tools into development workflows. LLMs analyze existing code and adapt their outputs based on the user's style and patterns, meaning that any shortcuts taken can become entrenched in future iterations. The takeaway is clear: developers should actively ensure that the code they write is maintainable, as the prompts fed into LLMs can significantly influence the quality of future coding assistance, potentially turning quick fixes into technical debt.