I know it isn’t your question exactly, and you probably know this, but the models for coding assist tools are generally fine tunes of models for coding specific purposes. Example: in OpenAI codex they use GPT-5-codex
I think the question is, can I throw a couple thousand bucks of GPU time at fine-tuning a model to have knowledge of our couple million lines of C++ baked into the weights instead of needing to fuck around with "Context Engineering".
Like, how feasible is it for a mid-size corporation to use a technique like LoRA, mentioned by GP, to "teach" (say, for example) Kimi K2 about a large C++ codebase so that individual engineers don't need to learn the black art of "context engineering" and can just ask it questions.
I'm curious about it too. I think there are two bottlenecks, one is that training a relatively large LLM can be resource-intensive (so people go for RAGs and other shortcuts), and making it finetuned to your use cases might make it dumber overall.
