My frustrations with my AI Buddies

Using AI code generation tools can feel like having a supercharged assistant—they can quickly draft code snippets, suggest solutions, and even build entire components. But as helpful as they are, I’ve found some frustrating limitations that often slow me down rather than speed me up.

One of the biggest issues I run into is that AI tools can’t remember my personal coding style. Every developer has their own way of structuring code, naming variables, and organizing logic. I might prefer concise functions or certain design patterns, but AI generators typically offer generic solutions that don’t align with how I like to write code. This means I often spend extra time rewriting or tweaking the output to match my style, which defeats the purpose of using these tools for efficiency.

Another pain point is that AI tools don’t remember which frameworks or CSS libraries I’m using. If I’ve chosen React with Tailwind CSS for a project, the AI might still suggest solutions using plain CSS or even other frameworks entirely. It’s frustrating to constantly correct the suggestions, reminding the tool that I’ve already made specific technology choices. This disconnect slows down development and forces me to double-check everything for compatibility.

I also like using the Context API in React, particularly putting fetch calls directly in the Provider for better encapsulation and centralized data management. However, AI often suggests creating standalone service files instead, which doesn’t align with my approach. This mismatch forces me to rewrite the AI-generated code to fit my preferred architecture.

Then there’s the issue of file organization. I like to use a feature-based folder structure, keeping related components, hooks, and utilities together. But AI tools have no sense of where files should go. They’ll suggest creating a new file but won’t place it in the right folder or follow my project’s organization pattern. This leads to a messy file structure that I have to clean up manually, making the process more cumbersome.

Overall, while AI code generation has a lot of potential, it lacks the memory and context needed to be truly helpful. Without the ability to learn and adapt to my style, tools, and project structure, it often creates more work than it saves. Until these tools can bridge that gap, they’ll remain more of a novelty than a reliable development partner.

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