RAG and fine-tuning, the "brain upgrade" of large language models, which path should we choose? (Xiaobai Science Popularization)

When I was working on a project recently, I found that some clients were not very clear about the division between RAG and model fine-tuning. They insisted on fine-tuning the problem, even though it was obvious that the large language model (LLM) plus RAG could solve the problem. However, after specific communication, I found that they just didn't understand the actual uses of the two.

In fact, Retrieval-Augmented Generation (RAG) and Fine-Tuning are the two most commonly used LLM "brain upgrade" technologies. Although they can both improve the performance of the model, their working principles and applicable scenarios are very different. Today, I will talk in depth about these two technologies and figure out whether to choose RAG or fine-tuning in different situations.

What does RAG and fine-tuning do?

Imagine that LLM is a learned brain.

Fine-tuning is like giving this brain a "specialist training". We further train the pre-trained LLM with a smaller dataset that focuses on a specific field (such as medicine, law) or a specific task (such as sentiment analysis, summary generation). By adjusting the internal parameters of the model, it can become more proficient in a certain field or better at completing a certain task. Just like a generalist becomes good at diagnosing diseases after further training in medicine.

 RAG (Retrieval-Augmented Generation) is more like equipping this brain with a "super library" and a "quick-check assistant". When someone asks a question, the "assistant" will quickly retrieve relevant information from an external dynamic knowledge base (such as a corporate database, the latest news article), and then provide this information and the user's question to the LLM brain, allowing the brain to generate answers based on the latest and most specific information. This method does not require changing the structure of the brain itself (no need to retrain the model), but rather enhances the accuracy and timeliness of its answers by providing external information. Just like a well-read person, when answering a specific question, he can quickly consult the latest information to verify and improve it.

Main Differences

The core difference between RAG and fine-tuning lies in the way they process and utilize knowledge.

RAG relies on external dynamic data sources that can be updated in real time, allowing the model to always obtain the latest information, and updating the knowledge base does not require retraining the model.

Fine-tuning relies on a fixed data set. If the data or task changes, retraining is required, which is costly.

RAG is able to utilize external specific knowledge while maintaining the original general capabilities of the model.

Fine-tuning may sacrifice some versatility due to deep training on a specific dataset, resulting in the so-called "catastrophic forgetting".

In terms of resource requirements, RAG mainly requires investment in data retrieval infrastructure (such as vector databases), and the computing requirements in the reasoning phase are relatively low.

Fine-tuning requires a lot of computing resources during the training phase, but the model itself contains the required knowledge during the inference phase.

Therefore, it can be said that RAG is more suitable for scenarios where real-time information is needed and the information source changes dynamically, such as a customer service chatbot that needs to know the latest product information, or a news summary application that needs to capture the latest reports.

Fine-tuning is more suitable for scenarios where the task is highly specialized and requires a deep understanding of a certain field. For example, medical diagnosis requires the model to master a large amount of medical terminology and pathology knowledge, or legal document analysis requires familiarity with complex legal provisions.

in conclusion

Both RAG and fine-tuning are powerful tools for improving LLM capabilities, but they have different focuses and are not mutually exclusive. RAG is good at processing dynamic information with its flexibility and real-time performance; fine-tuning uses deep training to enable models to achieve excellent precision in specific areas. Only by understanding the core differences, advantages and disadvantages, and applicable scenarios of the two, and combining them with actual project requirements, data characteristics, and resource conditions, can we make the most sensible technical choice, and even consider combining the two to create a more powerful and more demand-oriented AI application.