What is the difference between Fine-Tuning and RAG for enterprise AI?

All answers to this question.

For dynamic data like legal compliance, RAG is almost always superior to fine-tuning. Fine-tuning "bakes" knowledge into the model's parameters, making it static and expensive to update. If your data changes weekly, you’d have to retrain constantly, which is a resource sink. Fine-tuning is best for teaching a model a specific "style," "tone," or complex "format" (like medical coding). RAG, on the other hand, acts like an open-book exam where the model looks up the latest info in real-time. It’s cheaper, easier to audit, and much more scalable for information-heavy tasks.

   Answered 2024-09-12 by Jessica Adams

Are you considering a hybrid approach? Sometimes teams use RAG for the data but fine-tune a small model to better understand the specialized vocabulary so the retrieval step itself becomes more accurate.

   Answered 2024-09-13 by Robert Thompson

• Robert, that’s an interesting thought! We are actually seeing that our base model struggles with some of our proprietary legal acronyms. By fine-tuning a small "adapter" using LoRA (Low-Rank Adaptation), we might be able to improve the model's linguistic understanding without needing to retrain the whole thing for the actual facts. I’ll look into how we can combine a fine-tuned embedding model with our existing RAG pipeline to get the best of both worlds.

     Commented 2024-09-15 by Christopher Evans

RAG is also much better for "source attribution." It allows the model to cite exactly which document the answer came from, which is vital for any legal or compliance use case.

   Answered 2024-09-16 by Linda Moore

• I agree with Linda. In legal work, an answer without a citation is useless. RAG provides that direct link to the source text which fine-tuning simply cannot do.

     Commented 2024-09-17 by Jessica Adams