Naive question, are there good tutorials/places that teach us to implement RAG and fine tune a model? I don't know if it's even feasible. At the moment I create AI workflows for the company I work at to (semi-)automate certain things. But it's not like I could fine-tune Claude. I'd need my own model for that. But would I need a whole GPU cluster? Or could it be done more easily.
And what about RAG? Is it hard to create embeddings?
I'm fairly new with the AI part of it all. I'm just using full-stack dev skills and some well written prompts.
Lot's of tools for each of those separately (RAG and fine-tuning). We're working on combining them but it's not ready yet.
You don't need a big GPU cluster. Fine-tuning is quite accessible via both APIs and local tools. It can be as simple as making API calls or using a UI. Some suggestions:
- https://getkiln.ai (my tool): let's you try all of the below, and compare/eval the resulting models
- API based tuning for closed models: OpenAI, Google Gemini
- API based tuning for open models: Together.ai, Fireworks.ai
- Local tuning for open models: https://unsloth.ai (can be run on Google Collab instances if you don't have local Nvidia GPUs).
Usually the building the training set and evaluating the resulting model is the hardest part. Another plug: Kiln support synthetic data gen and evals for these parts.
And what about RAG? Is it hard to create embeddings?
I'm fairly new with the AI part of it all. I'm just using full-stack dev skills and some well written prompts.