Fine-tuning vs. RAG, how to train generative AI with your company's data?

There is no doubt, generative AI is one of the most revolutionary technologies of the last few years. It offers numerous benefits that companies are taking advantage of, among other things, to have their own assistant. But for it to be truly useful, you need to train the Large Language Models (LLMs). For doing this, there are two different approaches: RAG (Retrieval Augmented Generation) and Fine Tuning.

Depending on the objectives of your project, as well as your data volume and the technical infrastructure of your company, it will be convenient to use one approach or the other. This is why, understanding their characteristics and how each one works, is essential for choosing correctly.

Why customize an LLMs

Let’s start with the basics. There are general models such as GPT or LLaMA that have been trained with public information and, for basic aspects, can be useful. But there is a moment when they may not be enough, since they lack access to internal documents, policies or knowledge bases of your company.

Customizing a Large Language Model consists of adapting its behaviour, knowledge and vocabulary to the company so that it better responds to its needs. In other words, your company can customize the language model to your specific context so its answers adjust better, instead of using a generic model. This way, it can improve the accuracy, relevance and effectiveness of its answers.

Given this, how can LLMs be customized? As we had previously mentioned, there are several ways:

• RAG (Providing dynamic context). It consists of providing the model with relevant information in real time.
• Fine Tuning (Modifying the model's weights). Through this approach, the LLM is trained with your company's own data so that it can learn new relationships.
• Combining both strategies. On the other hand, both strategies can be combined, maintaining coherence with real-time updated data.

How does RAF works

Let's pause to delve a little deeper into each of these strategies. RAG (Retrieval-Augmented Generation) allows language models to give answers based on external data without having to modify their internal parameters. In other words, the LLMs doesn't have to learn new data from scratch, but instead consults a knowledge base in real-time that has been provided to it, retrieves the information relevant to the query, and provides an answer. We can differentiate about four stages in its operation:

• Indexing. The documents provided to the LLMs are converted into vector representations through embeddings.
• Query. A user sends a query or request that will initiate the RAG system.
• Retrieval. The system will search that vector database for the relevant fragments based on the requested information.
• Generation. The language model, using the received information as context and its ability to generate text, provides a coherent and up-to-date answer.

This system reduces costs because it allows companies to have an artificial intelligence system aligned with their business without having to train a model from scratch. Therefore, it has advantages such as immediate and rapid updating, reduced cost, and transparency. However, depending on the quality of the indexing or document filtering performed by this model, incomplete or out of context answers may be generated.

How does fine tuning works

On the other hand, we find fine-tuning. In this other approach, the model does have to be trained with proprietary data to modify its behavior and ensure it aligns with your company's context. This allows for an even greater refinement of its responses. Within fine-tuning, we can find different levels:

• Full. It is the most in-depth of all, as all layers of the language model must be retrained.
• Partial. Adjusts only the upper layers, which allows for reduced resources and time.
• Adaptive. Adds new layers to the base model to allow for customization without altering the original structure.

Applying this model has certain requirements, as if you are going to train an AI with new data, you must take into account avoiding including personal or sensitive data. It also requires a solid infrastructure and continuous maintenance.

Fine tuning offers an accurate specialization, obtaining better results. This type of training is used in common cases such as:

• Customer service and chatbots. They are usually trained with a company's frequently asked questions, as well as on its services and products so that they can respond to user queries and offer appropriate customer service.
• Marketing and content creation. It’s also common to see it in this area, training them to adopt the specific brand style and be able to create personalized texts.
• Industry. Another way to use them is for predictive maintenance and detecting anomalies in machinery data.

In short, training a generative AI with internal company data is key to obtaining added value. Both RAG and fine-tuning offer different paths to achieve this, each with its own advantages and challenges. The choice will depend on your business objectives and available resources.