RAG Or Fine-Tuning?
There is a lot of confusion about when to apply which method.
RAG makes sense when you have a custom knowledge base and want a standard ChatGPT-like interface on top of it. RAG has multiple components to it and can be tricky to get right. However, it's definitely easier to implement than fine-tuning.
Fine-tuning makes sense when you have several supervised examples of request responses and are looking for a particular format for your responses. That is if you want the model to adapt to a particular type of response. For example, you can fine-tune a model to be good at a specific type of SQL code generation.
Sometimes, but not often, it makes sense to do both. Using something like Abacus AI makes applying either method on open-source and closed-source LLMs super simple.
Of course, we are particularly partial to open-source, especially if it can do the job!
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