Hello, I would like to take this opportunity and ask for help here, about using A.I. with my own codebase.<p>Context:
I missed [almost] the entire A.I. wave, but I knew that one day I would have to learn something about and/or use it. That day has come. I'm allocated in one team, that is migrating to another engine, let's say "engine A → engine B". We are looking from the perspective of A, to map the entries for B (inbound), and after the request to B is returned, we map back to A's model (outbound). This is a chore, and much of the work is repetitive, but it comes with its edge cases that we need to look out for and unfortunately there isn't a solid foundation of patterns apart from the Domain-driven design (DDD) thing. It seemed like a good use case for an A.I.<p>Attempts: I began by asking to ChatGPT and Bard, with questions similar to: "how to train LLM on own codebase" and "how to get started with prompt engineering using own codebase".<p>I concluded that, fine-tuning is expensive, for large models, unrealistic for my RTX 3060 with 6Gb VRAM, no surprise there; so, I searched here, in Hacker News, for keywords like "llama", "fine-tuning", "local machine", etc, and I found out about ollama and DeepSeek.<p>I tried both ollama and DeepSeek, the former was slow but not as slow as the latter, which was <i>dead slow</i>, using a 13B model. I tried the 6/7B model (I think it was codellama) and I got reasonable results and speed. After feeding it some data, I was on my way to try and train on the codebase when a friend of mine came and suggested that I use Retrieval-Augmented Generation (RAG), I have yet to try it, with a setup Langchain + Ollama.<p>Any thoughts, suggestions or experiences to share?<p>I'd appreciate it.