Fine Tuning vs RAG: Which One Makes Your AI Smarter?

In this blog, we will talk about fine tuning vs RAG.

They’re both powerful ways to enhance the capabilities of large language models.

So two of the biggest issues with dealing with generative AI right now are: enhancing the models and dealing with their limitations. For example, I just recently asked an LLM a simple question: Who became the European football champion in 2024? And while this might seem like a simple query for my model, well, there’s a slight issue. Because the model wasn’t trained on that specific information, it can’t give me an accurate answer.

At the same time, these popular models are very generalistic. But businesses usually need to specialize AI for specific use cases and adapt them in enterprise applications. Because your data is one of the most important things that you can work with. And in the field of AI, using techniques such as RAG or fine tuning will allow you to enhance the capabilities of LLM models and adapt a general AI model to your needs.

In this blog, we’re going to learn about both of these techniques, the differences between them and where you can use them.

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Retrieval Augmented Generation

Retrieval Augmented Generation is a way to increase the capabilities of a model through retrieving external and up to date information, augmenting the original prompt that was given to the model, and then generating a response back using that context and information.

Let’s consider our previous example about a European football champion 2024. While the model didn’t have the information and context to provide an answer; this was one of the big limitations of LLM’s. But this is mitigated with a RAG, because now instead of having an incorrect or possibly hallucinated answer, we’re able to work with what’s known as a corpus of information.

How does it work?

• A corpus of information could be data, PDFs, documents, spreadsheets, and other things that are relevant to specific organization or knowledge that we need to specialize in.
• When the query comes in, the retriever gets activated and pulls the correct information from the corpus to present relative context to what the question or prompt is.
  
• The retriever then passes that knowledge, along with the original prompt, to a large language model.
  With its intuition and pre-trained data, the model is able to give us a response back based on that contextualized information, which is accurate and up-to-date.
  
• Because of this, we  start getting better responses back from a model using our proprietary and confidential information, without needing to do any retraining on the model. This becomes a great and popular way to enhance the capabilities of a model, without having to do any fine tuning.

Fine-Tuning

Model fine tuning is taking a general pre-trained model and training it at least to focus on one specific area or biases inside the neural network. What this typically looks like is taking a pre-trained existing model like GPT-3 and fine-tuning it to make GPT-3.5 Turbo.

How fine-tuning works

• Take a large language foundational model.
• Specialize it to focus on one specific area or biases inside the neural network by training it using labeled and targeted data.
• When queried by a user, the specialized model is able to deliver responses in a certain style and have a certain tone that aligns with the company or brand voice because the context and intuition are baked into the model itself. That context becomes part of the model’s weights, rather than being supplemented on top with a technique like RAG.

So we understand how both of these techniques can enhance a model’s accuracy, output and performance. But let’s take a look at their strengths and weaknesses and some common use cases. Because the chosen technique can greatly affect a model’s performance, its accuracy, outputs, compute cost, and much, much more.

Strengths and Weaknesses of RAG

Retrieval Augmented Generation  is perfect for dynamic data sources such as databases, and other data repositories where we want to continuously delete older information and push up to date information for the model to use and understand.

A RAG system uses a retriever to get relevant information from a corpus and pass in the information as context in the prompt that really helps with hallucinations. And providing the sources for this information is really important in systems where we need trust and transparency when we’re using AI.

In RAG, having an efficient retrieval system is really important in selecting and picking the data that we want to provide in that limited context window that AI can read at one time. Because of this limit, the system needs to be well maintained and carefully managed to keep the answers accurate and useful.

With this approach, we effectively supplement information on top of the model. We’re not essentially enhancing the base model itself, we’re just giving it the relative and contextual information it needs.

Strengths and Weaknesses of Fine Tuning

In fine tuning, we bake in the context and intuition into the model influencing how the model behaves and reacts in different situations. Whatever we want the model to do like adjusting insurance or summarizing docs or something else, we can use fine tuning in order to specialize it in that process.

As data is baked into the model’s weights itself therefore response speed increases and inference cost reduces. For example, we can use smaller prompt context windows in order to get the responses that we want from the model. And as we begin to specialize these models, they can get smaller and smaller for our specific use case.

Fine tuning is best for running these specific, specialized models that are trained for specific tasks. But at the same time we have the same issue of cutoffs. The model only knows information until the point where it is trained, after that, it has no more additional information. So the same issue that we had with the World Cup example.

Fine Tuning vs RAG

How to Choose Between RAG and Fine-Tuning

So when you’re thinking about choosing between RAG and fine tuning, it’s really important to consider your AI enabled applications, priorities and requirements. So namely this starts off with the data. Is the data that you’re working with slow moving or is it fast?

For example, if we need to use up to date external information and have that ready contextually every time we use a model, then this could be a great use case for RAG. For example, a product documentation chatbot where we can continually update the responses with up to date information.

Fine tuning is really powerful for specific industries that have nuances in their writing styles, terminology, vocabulary. For example, if we have a legal document summarizer this could be a perfect use case for fine tuning.

Now let’s think about sources. This is really important in having transparency behind our models. 

As RAG is able to provide the context and source of information. So this could be a best use case for chatbot for retail insurance and a variety of other specialties where having that source and information in the context of the prompt is very important.

But at the same time, an organization may have past data that can be used to train a model, so it becomes accustomed to the data it will be working with. For example, a legal summarizer can be trained on past legal cases and documents so that it understands the context it is working in and produces better, more desirable outputs.

So this is effective, but I think the best situation is a combination of both of these methods. So let’s say we have a financial news reporting service. Well, we could fine tune it to be native to the industry of finance and understand all the lingo there. We could also give it past data of financial records and let it understand how we work in that specific industry, but also be able to provide the most up to date sources for news and data and be able to provide that with a level of confidence and transparency and trust to the end user who is making that decision and needs to know the source.

The combination of fine tuning and RAG is awesome, because we can really build amazing applications taking advantage of both RAG as a way to retrieve that information and have it up to date, but fine tuning to specialize our data, but also specialize our model in a certain domain.

So, they’re both wonderful techniques, and they have their strengths, but the choice to use one or combination of both techniques is up to you and your specific use case and data.

FAQs about Fine Tuning vs RAG

1. What is the main difference between RAG and fine-tuning?

RAG is a way to increase the capabilities of a model through retrieving external and up to date information, augmenting the original prompt that was given to the model, and then generating a response back using that context and information. Whereas, Model fine tuning is taking a general pre-trained model and training it at least to focus on one specific area or biases inside the neural network.

2. When should I use Retrieval Augmented Generation (RAG)?

RAG is the best technique if we need to use up-to-date external information and have that ready contextually every time we use a model. As RAG is able to provide the context and source of information. So this could be a best use case for chatbot for retail insurance and a variety of other specialties where having that source and information in the context of the prompt is very important.

3. Does RAG reduce hallucinations more than fine-tuning?

Yes. RAG reduces hallucinations because it uses a retriever to get relevant information from a corpus and pass in the information as context in the prompt.

4. Can RAG and fine-tuning be used together?

Yes. The combination of fine tuning and RAG is awesome, because we can really build amazing applications taking advantage of both RAG as a way to retrieve that information and have it up to date, but fine tuning to specialize our data, but also specialize our model in a certain domain.