RAG vs. Fine Tuning: Which One is Right for You?

Introduction

In today’s world, LLMs are everywhere, but what exactly is an LLM and what are they used for?

LLM, an acronym for Large Language Model, is an AI model developed to understand and generate human-like language. LLMs are trained on huge data sets (hence “large”) to process and generate meaningful and relevant responses based on the input they receive from an interaction. These data sets come from various sources, from websites to books, articles, and other text-related resources. 

The Basics of Retrieval Augmented Generation (RAG)

But while LLMs are incredibly powerful, they also come with limitations. One of the most common limitations of LLMs is hallucinations. Hallucinations occur when an AI model fabricates a confident but inaccurate response. This issue can be caused by many factors, including divergences in the source content when the data set is incredibly vast or flaws with how the model is trained. The latter can even cause a model to reinforce an inaccurate conclusion with its previous responses. This often raises concerns about its potential to spread misinformation or disinformation if not used responsibly.

But there is always a solution for every problem. In this article, we will cover the two most common ways to reduce hallucinations in LLMs. 

• • RAG (Retrieval Augmented Generation)

• • Fine-tuning

RAG is one of the solutions to improving LLM hallucinations; just like the parrot we mentioned earlier, they can’t understand everything you say. LLMs have their limits too. They are limited to just what they are trained with, so it becomes a problem when you ask the parrot a question outside of what he normally mimics, or in this case, you ask the model a question outside of what it is trained with. A clearer example with the parrot is if a stranger comes into the house and asks the parrot, ‘How many planets are there?’, the parrot will be confused because this is not the usual household question of “How are you?” or “How hot is it?” that it’s used to. The parrot is bound to answer, but the answer will most likely be incorrect.

Source: Infolob.com

Retrieval Augmented Generation, or RAG, is a framework to help large language models be more accurate and up-to-date by instructing the models to pay attention to primary source data before giving their response, which makes them less likely to hallucinate because they’re less likely to rely only on information they learned during training. It also allows us to get the model to have a behavior that can be very positive, which is knowing when to say I don’t know. If the user’s question cannot be relatively answered based on the data store, the response name will be “I don’t know” instead of making up something that is believable and wrong.

This can have a negative effect as well, because if the Retriever or RAG system is not sufficiently good enough to give a large language model quality information, then users are bound to not receive the answers they are looking for.

Fine-Tuning Explained: Customizing Large Language Models for Specific Needs

Fine-tuning is another way to improve LLM models. Fine-tuning is the process of training the parameters of a pre-trained large language model to perform a specific task. Although pre-trained language models like GPT possess vast language knowledge, they lack specialization in specific areas. Fine-tuning addresses this limitation by allowing the model to learn from domain-specific data, making it more accurate and effective for targeted applications. To use an analogy here, GPT-3 is like a raw diamond right out of the earth; fine-tuning is taking this raw diamond and transforming it into a usable diamond (ChatGPT).

By exposing the model to task-specific examples during fine-tuning, it can gain a deeper understanding of the target domain.

source: mindbowser.com

There are three processes involved in fine-tuning a model: self-supervised learning, supervised learning, and reinforcement learning. 

• • Self-supervised learning is the way normal large-based models are trained; the models are trained in a self-supervised manner with a data set of texts; in other words, you take a sequence of text, feed it into a model, and have the model complete the sentence. What differentiates self-supervised fine-tuning from just training a model is that you can curate your training data to align with the specific domain of your choice.

• • Supervised learning is where we have a training dataset consisting of inputs and corresponding outputs; in other words, we train a model with a question-answer pair dataset. A prompt template is normally used in this process of fine-tuning.

• • Reinforcement learning helps to better align model outputs with ideal human behavior, especially for conversational use cases like chatbots. This system of learning operates like a reward system, whereby a good output from the model is rewarded with a good score while a bad output is rewarded with a bad score.

It is essential to fine-tune base models because we need the models to generate a much more desirable output. Beyond performance, it was demonstrated by OpenAI that a smaller, fine-tuned model can outperform large base models.

RAG vs Fine-Tuning: A Comparative Analysis

While both RAG and fine tuning have their advantages, they have their limitations as well. Let us analyze and compare both advantages and disadvantages. 

RAG enriches responses with accurate, up-to-date information from external databases but may not tailor the linguistic style to user preferences without additional customization techniques. 
Fine-tuning requires a well-curated dataset specific to the task and less external data infrastructure than RAG, but relies on high-quality domain-specific training data. 
RAG is cost-effective, efficient, and scalable for applications needing access to the latest information or diverse topic coverage.
Fine-tuning demands a significant initial investment in time and resources but operates efficiently within specialized domains; scalability requires additional fine-tuning rounds for new domains.
RAG fetches relevant information from external databases across a wide range of domains; fine-tuning achieves depth and precision within a specific domain.

Advanced Applications: Where RAG Excels

Currently, RAG is one of the most used methods in the domain of large language models; one can even assume that 90% of LLM-powered applications make use of RAG in one way or another. Here are some applications in which RAG is most commonly used.

1. Customer Support Systems: RAG helps respond to conversational AI systems by utilizing external knowledge sources. A company can easily have a specialized AI system trained on the company’s profile or manual as a customer support system. This trained AI system can offer tailored solutions, answer specific questions, and even predict potential customer needs based on context and retrieved information. This can effectively replace L1 and even L2 customer support if built properly.

2. 2. Content Generation: Marketing teams could use RAG to automatically create original content to drive traffic, invite potential customers, outreach, expose product features, and other elements that may raise engagement rates and conversion rates. Custom RAGs can be trained to a brand’s specific content collection, like blog posts, website copy, and also old internal documents about the brand’s profile. This allows the LLM to capture the unique voice and style of the brand, ensuring consistency and brand alignment across all content. Through RAG, the LLM can also tailor keywords for SEO to align with the brand’s knowledge base.

3. 3. Document assistance: When LLM is provided with an enterprise document, rest assured that the responses are limited to what is provided within them. A retriever system can analyze the documents efficiently, summarize key points, and highlight relevant sections based on user queries. This procedure saves a lot of time and is more accurate. RAG also allows you to build and train models over external documents like national regulations, compliance, etc.

4. 4. Fraud detection: RAG Systems can check financial transactions by millions and compare them across different sources. Apart from processing real-time data, RAG integrates historical information, increasing the effectiveness of fraud detection by searching through similar patterns, and then having an LLM evaluate the pattern in sequence to identify whether a transaction made is fraudulent or not. It may be used to prevent financial losses and security breaches when sensitive information is at risk.

Integrating RAG and Fine-Tuning: Synergistic Approaches

Source: OpenAI community

For some applications, optimal performance requires leveraging both external knowledge through RAG and domain adaptation via fine-tuning. This can be termed a hybrid approach because it blends the benefits of RAG and fine-tuning into a single pipeline. RAG provides fast access to fresh external data and, combined with a fine-tuned model, generates output that utilizes both external context and fine-tuned domain knowledge.

Practical Considerations: Model Size and Computational Resources

LLMs require lots of resources because they are constantly calculating the probabilities of an output. One of the resources they draw computing power from is graphics processing units (GPUs). A GPU is an electronic circuit that can perform mathematical calculations at high speed. They are specially designed to handle complex parallel processing tasks, making them perfect for training LLMs.

These high computational requirements often lead to a high cost of operation for these models, so it’s no no-brainer that the smaller the models, the less cost we incur. But at the same time, we need our model to be as accurate, fast, and efficient as it can be. This is where RAG comes into play, as it eliminates the need to train our model on a large set of data, which would result in numerous parameters. Instead, it allows us to provide data storage, enabling our model to retrieve any required data at any given time.

We can take a look at the cost of hosting and deploying the conversation model LLama 70b on cloud platforms. According to 1001epochs.ch, you can expect to pay between $0.53 and $7.34 per hour. The cost of hosting the LlaMA 70B models on the three largest cloud providers is estimated in the figure below.

Fine-tuning vs. RAG: Making the Right Choice

Having to choose the way your base model is optimized really matters because you can end up with a model that is ineffective, costly, and hard to manage if the wrong choice is made.

When deciding between RAG and fine-tuning, it’s important to understand their strengths and weaknesses. If you need help deciding between RAG and fine-tuning, consider this: RAG stands out for its ability to stay updated with changing data and for offering transparency to explain its answers. As new information becomes available, RAG can adapt and add to its response, ensuring that it remains relevant. In addition to this, RAG offers transparency to explain its answers, allowing users to understand how it arrived at its answers. When explanation and understanding are required, this can be invaluable.

On the other hand, fine-tuning shines when you have a lot of specific data and you want customized behavior from your model. By fine-tuning the parameters of the pre-trained model on your specific data set, you can optimize its performance for your particular business or domain. This optimization can achieve better results compared to using a more general model such as RAG, especially when dealing with niche or specialized sectors.

The decision between RAG and fine-tuning depends on your specific needs and constraints. Consider factors such as the availability and quality of your data, the need for real-time adjustments, the need to interpret the model, and the resources you can devote to model development and maintenance. By carefully weighing each of these items, you can choose the best option that suits your needs.

The Future of AI with RAG: Expanding Horizons

With the combination of a knowledge base for language models and the precision of retrieval-based information, RAG is already proving to revolutionize our interactions with AI. It has transformed AI into a more trusted, accurate, and forward-thinking partner in our technological journey, opening up new possibilities for how we engage with artificial intelligence.

In today’s technology era, RAG represents a significant leap forward, ensuring that our journey with AI continues to be as enriching and productive as possible. As we continue to explore and implement these advancements, the future of AI looks brighter and more promising than ever.

RAG is currently focused on enhancing text-based language models by leveraging external data, but there is a possibility of now also expanding its horizons towards a multimodal future.

Multimodal RAG integrates various types of data, such as images, audio, and video, alongside text, allowing AI models to generate responses from sources that are not only text-based but also enriched by visual and auditory contexts.

So this will be like teaching models to see, hear, and understand the world in a more human-like way, across all sorts of sensory channels. So buckle up, because the future of AI is looking more colorful and multi-dimensional than ever before!

Conclusion: Embracing the Strengths of RAG

In conclusion, RAG is a robust solution to the limitations faced by LLMs. The integration of external knowledge sources with the ability of real-time data retrieval has been shown to increase model response accuracy and timeliness practically. 

However, fine-tuning has its advantages in cases where domain-specific data is available. The ability to train model parameters for a specific task or domain yields high performance, for example, in specialized domains. RAG’s capability to be customized to meet the desired model behavior and customized performance for certain tasks is essential. 

In the future, the advancement of LLMs will involve the utilization of the strengths of both RAG and fine-tuning. A hybrid approach, which leverages external context from RAG with internal domain-specific expertise from fine-tuning, could be the ideal strategy for optimizing the performance of AI systems. By capitalizing on the numerous benefits of both techniques and tailoring them to individual use cases, we can ensure optimal innovation, efficiency, and success in the changing world of AI.