>> But right now, the best way to help an LLM is have a deep understanding of the problem domain yourself, and just leverage it to do the grunt-work that you'd find boring.

This is exactly how I use it. I prefer Gemini 3 personally.

I try to learn as much as I can about different architectures, usually by reading books or other implementations and coding first principals to build a mental model. I apply the architecture to the problem and the AI fills in the gaps. I try my best to focus and cover those gaps.

The reason I think it is inconsistent in nailing a variety of tasks is the recipe for training LLMs, which is pre-training + RL. The RL environment sends a training signal to update all the weights in its trajectory for the successful response. Karpathy calls it “sucking supervision through a straw”. This breaks other parts of the model.