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I’ve been building a full ecosystem with LLMs for almost a year now, and it’s become a complex kind of mental gymnastics. Having to constantly ask the model to re-explore the entire codebase (which is getting heavier and heavier), and making sure it really explores the code instead of just giving a rough overview, is honestly exhausting day-to-day. I’ve noticed that in the end, I’ve spent more time improving how LLMs work than they’ve actually worked for me.
But it’s helped me structure my workflow, and I recently made a decision that changed the way I do things. I established strict patterns like route/services/hooks/UI or /services/hooks/UI depending on the needs. I have a document that explains my whole architecture, my style, my paths, and most importantly how to implement new features. It lets me think the same way every time, use what’s already in my code without reinventing unnecessary stuff or overloading the code, etc. etc.
It’s a time sink, but it helps to understand how each LLM works. I switch models depending on the needs. Now I know when Claude, GPT, or Gemini is better, and I hope to make the most of them. In the end, the real conclusion is that to get the result you want, you need to learn and be solid in your learning to always make sure even the best LLM doesn’t just spout nonsense. (PS: I use agents like Copilot sparingly because I want every line to be exactly how I imagined it, not how the LLM imagined it.) 0 reply
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