RAG vs fine-tuning: two approaches to customising generative AI models

In the evolving landscape of generative artificial intelligence, model customisation has taken on a central role. Two main approaches are competing for the spotlight: fine-tuning and Retrieval Augmented Generation (RAG). In this article we analyse the features, benefits and limits of both, to help you understand which approach to adopt for your needs.

What is fine-tuning?

Fine-tuning is a technique that involves training an existing generative AI model on a specific dataset. This method makes it possible to adapt the model to particular tasks or domains, using additional data to refine its knowledge and improve its performance on specific goals and tasks.

Pros and cons of fine-tuning

Pros

• High customisation, since the model is optimised for the specific task and can generate extremely relevant outputs, that is, answers.

• Quality of results: with high-quality data, fine-tuning can significantly improve performance compared with a generic model.

• Persistent memory: the knowledge learned during fine-tuning is built into the model itself and remains available even without access to the original data.

Cons

• High costs: training a model requires significant computing resources, such as latest-generation GPUs or TPUs, which speed up training given the size of the models.

• Complex maintenance: every data update requires a new training cycle, increasing costs and the time required.

• Risk of overfitting: if the dataset is too small or unrepresentative, the model may overfit to the training data, losing its ability to generalise.

What is Retrieval Augmented Generation (RAG)?

RAG is an alternative approach that uses pre-trained models, also called foundation models, combining them with an information retrieval system. Instead of retraining the model, RAG provides the model with dynamic context from an external database (for example documents, knowledge bases or APIs). This context is used to improve the quality of the generated answers.

The limits of RAG

1. Dependence on the retrieval system: the quality of the answers depends on the relevance and accuracy of the retrieved data

2. Latency: retrieving information from a database can delay responses, especially with large volumes of data

3. Limited context: pre-trained models may have limits in handling large amounts of supplied context.

Why is RAG overtaking fine-tuning?

In the past, fine-tuning was the dominant strategy for adapting models to specific tasks. However, as models grew in complexity and size, training costs became prohibitive for many organisations. At the same time, the growing availability of advanced pre-trained generative models (for example GPT-4, Llama or Mistral) has made it less necessary to work directly on the model’s weights.

RAG, by contrast, offers a more agile and scalable solution. Rather than trying to adapt the model itself, this approach focuses on enriching the context given to the model. This makes it possible to fully exploit the potential of already-trained models, avoiding the associated costs and risks.

How to choose between fine-tuning and RAG

Fine-tuning is ideal if:

1. You have a highly specific task that requires extreme precision

2. You have a well-structured and representative dataset

3. You are willing to invest in computing resources to ensure the model’s quality.

Some examples of use:

• Medical applications that require accurate diagnoses

• Business automation systems with specific technical language

• Specialised translation in sector-specific domains.

RAG is preferable if:

1. You need dynamically updated answers without retraining the model

2. You want to optimise costs and reduce implementation time

3. You work in fast-changing contexts, where content changes frequently.

Some examples of use:

• Customer support systems that draw on company knowledge bases

• Custom search engines for specific sectors

• Dynamic reports based on data updated in real time.

The choice between RAG and fine-tuning therefore depends on the specific needs of the project. To recap: if you need extreme precision on a specific task and have the necessary resources, fine-tuning could be the ideal solution. If, instead, you are looking for flexibility, cost efficiency and automatic updates, RAG is a valid alternative.

As technology evolves and pre-trained models become more widely available, RAG is becoming the preferred approach for many applications. Understanding these two paradigms is essential to making the most of generative AI in your work or business.

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