We’ve had a few questions about how RAG compares to LLM fine-tuning. Here’s the breakdown:

How Are They Different?

Both RAG and LLM fine-tuning allow institutions to enhance their LLMs, but in different ways. Fine-tuning modifies an LLM’s internal parameters using additional training data. RAG, on the other hand, supplements the model’s internal memory with non-parametric data retrieved from external sources.

Which One Is Better?

Fine-tuning can work well for specific tasks that don’t need constant updates, but RAG is better when information changes frequently. For dynamic environments, RAG’s ability to retrieve up-to-date information in real time is a big advantage.

Are There Other Benefits of RAG?

RAG facilitates adherence to data security and privacy policies by retrieving information from approved and trusted sources only. Supplementing data through fine-tuning can introduce risks if not managed carefully.

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