RAG vs. Fine Tuning

Let's talk about RAG versus fine-tuning. Both are powerful ways to enhance the capabilities of large language models, but today you'll learn about their strengths, their use cases, and how to choose between them. One of the biggest issues with generative AI right now is not only enhancing the models, but also dealing with their limitations. For example, I recently asked my favorite LLM a simple question: Who won the Euro 2024 World Championship? While this might seem like a straightforward query, there's a slight issue because the model wasn't trained on that specific information. It can't give me an accurate or up-to-date answer. At the same time, these popular models are very generalistic. How do we think about specializing them for specific use cases and adapting them for enterprise applications? Your data is one of the most important assets you can work with, and in the field of AI, using techniques such as RAG or fine-tuning will allow you to supercharge the capabilities your application delivers. In the next few minutes, we're going to learn about both of these techniques, the differences between them, and where you can start seeing and using them. Let's get started.

Let's begin with retrieval-augmented generation, which is a way to increase the capabilities of a model by retrieving external and up-to-date information, augmenting the original prompt given to the model, and then generating a response using that context. This is really powerful because, if we think back to the example with the Euro Cup, the model didn't have the information in context to provide an answer. This is one of the big limitations of LLMs. However, this is mitigated with 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. This could be data, PDFs, documents, spreadsheets—things that are relevant to our specific organization or knowledge that we need to specialize in.

When the query comes in, we're working with what's known as a retriever that's able to pull the correct documents and relevant context for the question, and then pass that knowledge, along with the original prompt, to a large language model. With its intuition and pre-trained data, it's able to give us a response based on that contextualized information. This is really powerful because we can get better responses from a model using our proprietary and confidential information without needing to do any retraining. This is a great and popular way to enhance a model's capabilities without having to do any fine-tuning.

As the name implies, fine-tuning involves taking a large foundational language model and specializing it in a certain domain or area. We're working with labeled and targeted data that's provided to the model, and after some processing, we end up with a specialized model for a specific use case. This allows the model to communicate in a certain style or tone that could represent our organization or company. When the model is queried by a user or through any other method, it will respond with the correct tone, output, or specialty in the desired domain. This is really important because we're baking this context and intuition into the model itself, making it part of the model's weights, as opposed to supplementing it on top with a technique like RAG. We understand how both of these techniques can enhance a model's output and performance, but let's take a look at their strengths and weaknesses in some common use cases. The direction you choose can greatly affect a model's performance, accuracy, outputs, compute cost, and much more.

Let's begin with retrieval-augmented generation. One thing to point out is that because we're working with a corpus of information and data, this is perfect for dynamic data sources such as databases and other data repositories, where we want to continuously pull information and keep it up to date for the model to use and understand. At the same time, because we're working with this retriever system and passing the information as context in the prompt, it really helps with hallucinations. Providing sources for this information is crucial in systems where we need trust and transparency when using AI. This is fantastic, but we also need to consider the entire system. Having an efficient retrieval system is important for selecting and picking the data we want to provide within the limited context window, and maintaining this is something you need to think about. What we're doing here is effectively supplementing information on top of the model. We're not enhancing the base model itself; we're just giving it the relevant and contextual information it needs.

Fine-tuning is a little bit different because we're baking that context and intuition into the model itself. We have greater influence over how the model behaves and reacts in different situations. Is it acting as an insurance adjuster? Can it summarize documents? Whatever we want the model to do, we can use fine-tuning to help with that process. At the same time, because that information is baked into the model's weights, it's really great for speed, inference cost, and a variety of other factors involved in running models. For example, we can use smaller prompt context windows to get the responses we want from the model. As we begin to specialize these models, they can become smaller and more efficient for specific use cases. This is really beneficial for running specialized models in a variety of scenarios. However, we still face the same issue of information cutoff up to the point where the model was last trained. After that, we can't provide any additional information to the model, which is the same issue we saw with the World Cup example.

Both of these approaches have their strengths and weaknesses, but let's look at some examples and use cases. When you're deciding between RAG and fine-tuning, it's important to consider your AI-enabled application's priorities and requirements. This starts with the data. Is the data you're working with slow-moving, or is it fast-changing? For example, if we need to use up-to-date external information and have that contextually available every time we use the model, this could be a great use case for RAG. A product documentation chatbot, for instance, can continually update its responses with the latest information. At the same time, consider the industry you are in. Fine-tuning is especially powerful for industries with specific nuances in writing styles, terminology, and vocabulary. For example, if you have a legal document summarizer, that could be a perfect use case for fine-tuning. Now, let's think about sources. This is increasingly important for transparency in our models. With RAG, being able to provide the context and indicate where the information came from is extremely valuable. This could be a great use case for a chatbot in retail, insurance, or other specialties where having the source of information in the context of the prompt is very important. On the other hand, you may have past data within your organization that you can use to train a model, allowing it to become accustomed to the data it will encounter. For example, that legal summarizer could be trained on past legal cases and documents, so it understands the situations it will face and produces better, more desirable outputs.

This is all great, but I think the best situation is a combination of both methods. Let's say we have a financial news reporting service. We could fine-tune the model to be native to the finance industry and understand all the relevant terminology. We could also provide it with past financial records so it understands how we work in that specific industry. At the same time, we can provide the most up-to-date sources for news and data, delivering information with confidence, transparency, and trust to the end user who needs to know the source. This is where a combination of fine-tuning and RAG is especially powerful, because we can build amazing applications that take advantage of RAG to retrieve up-to-date information, while using fine-tuning to specialize the model for a certain domain. Both techniques are valuable and have their strengths, but the decision to use one or a combination of both depends on your specific use case and data. Thank you very much for watching. As always, if you have any questions about fine-tuning, RAG, or any AI-related topics, let us know in the comment section below. Don't forget to like the video and subscribe to the channel for more content. Thanks again for watching.