LLM Fine-Tuning vs. RAG: When to Use Which
Lately, the question I get most often while working on AI projects is, "Should I use fine-tuning or RAG?" So I put together a quick summary.
1. RAG (Retrieval-Augmented Generation)
- A method that retrieves relevant information from external documents/DBs and feeds it into the LLM
- Strong for up-to-date information, proprietary company data, and frequently changing content
- Low setup complexity and relatively low cost
- Downsides: search quality determines overall performance, and you need to account for token length limits
2. Fine-Tuning
- Training the model's weights further on domain-specific data
- Good for enforcing a fixed format, maintaining a specific style/tone, and using domain terminology
- Requires data preparation and training infrastructure, so upfront costs are high
- Downsides: difficult to update knowledge after training, and there's a risk of data contamination
Conclusion: The standard approach is to apply RAG first, and if there are still gaps (like response format or technical terminology), mix in fine-tuning. I've tried both, and it felt like RAG solved about 80% of the problem, with the remaining 20% filled in by fine-tuning. Don't overthink it on your own—I recommend starting with RAG.
7 answers
Agreed, starting with RAG is the way to go.
As someone who's tried fine-tuning, I get that 20% ratio on a gut level lol
Well, I've found that if RAG search quality is poor, fine-tuning can sometimes be the better option.
Oh, I didn't know that. I didn't realize the token length limit would be such a big bottleneck.
It's a legal domain, but RAG alone wasn't enough, so we ended up mixing in fine-tuning. The specialized terminology is definitely different.
Isn't that conclusion too simple? RAG is the right call in the early stages when data is scarce, but once you've accumulated enough, switching to fine-tuning is the standard approach.
So good, I'll save this and check it out later.