zlacker

I am a little bit confused whether I need fine-tuning or RAG for my use case? My use case is this: I have some private data (say 1000 word documents), I want a QA capability on those 1000 documents. What is the best approach? Any help is appreciated.

>>logana+(OP)
Look at it like this:

- Fine tuning: Difficult, time-consuming, slow, takes time to add new information, costs a lot more.

- RAG: Can be free if you use free options like Chrome, Weaviate, or Postgres with Vector Plugin. Really fast. Once you set it up, you just need to upload a document, and it's available for GPT to answer with.

I'm using RAG for a client right now, and it was a breeze. Really easy, especially if you use something like Langchain. Compared to fine-tuning, it's a lot easier, cheaper, and faster...