Retrieval Augmented Generation Vs Fine Tuning LLM: Easy Guide

Do you want to know the difference between Retrieval Augmented Generation vs. fine-tuning LLM?… If yes, this blog is for you. In this blog, I tried to explain Retrieval Augmented Generation vs. fine-tuning LLM most simply.

Now, without further ado, let’s get started-

Retrieval Augmented Generation Vs Fine Tuning LLM

First, let’s understand What is Retrieval Augmented Generation (RAG) in AI.

What is Retrieval Augmented Generation (RAG) in AI?

Imagine you’re writing a story, and you want to include some cool facts or information to make it more interesting. But, here’s the catch: you’re not sure what facts to add or where to find them. That’s where RAG comes in.

Think of RAG like having a super-smart assistant who can help you find just the right information you need, exactly when you need it. It’s like having Google, but way smarter.

How Retrieval Augmented Generation (RAG) Works?

1. Generation: First, you start by generating some text, like a question or a statement. For example, you might write, “Tell me about space travel.”
2. Retrieval: Then, RAG springs into action. It goes through a huge database of information, kind of like flipping through a gigantic bookshelf filled with books about everything under the sun. It finds the most relevant information related to your query. So, for our space travel example, RAG might find information about different space missions, astronauts, and how rockets work.
3. Augmentation: Finally, RAG takes all that juicy information it found and adds it to your original text. It’s like your story just got a major upgrade with all these cool facts and details.

So, with RAG, you can create content that’s not only well-written but also packed with accurate and interesting information. It’s like having a knowledgeable friend by your side whenever you’re writing, ready to help you make your work shine. And that’s Retrieval Augmented Generation in a nutshell!

Now, let’s understand What is Fine Tuning in LLM.

What is Fine Tuning in LLM?

Think of a language model like a really smart friend who knows a lot about everything. Let’s call this friend GPT (Generative Pre-trained Transformer). GPT has read tons of books and articles, so it knows a lot about how language works. But sometimes, even though GPT is really smart, it might not be exactly what we need for a specific task.

That’s where fine-tuning comes in! Fine-tuning is like giving your super-smart friend a little extra training just for the job you have in mind. Let’s say you want GPT to help you figure out if a movie review is positive or negative. You’d take GPT and give it some examples of movie reviews, teaching it to understand the difference between a thumbs-up and a thumbs-down.

How Fine Tuning Works:

1. Task Definition: First, you decide what you want GPT to do. In our case, it’s understanding movie reviews.
2. Selection of Pre-trained Model: You pick GPT as your starting point because it’s already super knowledgeable.
3. Dataset Preparation: You gather a bunch of movie reviews to train GPT on. These reviews will be like the homework for GPT to learn from.
4. Fine-tuning Procedure: This is where the training happens! You let GPT read through the movie reviews and adjust its understanding based on whether they’re positive or negative. It’s like teaching GPT to become a movie critic!
5. Evaluation: After GPT has done its homework, you check to see how well it’s learned. You give it some new movie reviews it hasn’t seen before and see if it can correctly tell you if they’re thumbs-up or thumbs-down.
6. Deployment: Once GPT has learned from its homework, you can put it to work! You can ask it to analyze new movie reviews and give you its expert opinion.

So, in simple terms, fine-tuning is like giving your super-smart friend a little extra training to become an expert in a specific area. It’s pretty helpful because it helps us make the most out of these language models like GPT and use them for all sorts of tasks!

Enroll in- Introduction to Large Language Models by Coursera

I hope now you understand what is RAG and what is Fine Tuning. Now, let’s see the difference between both.

Retrieval Augmented Generation Vs Fine Tuning LLM

LLM is like a big language playground. You start with this huge collection of words and sentences that a computer already knows. It’s similar to when you learn to talk by hearing people around you. Then, with fine-tuning, we tweak this playground a bit to make it better at a specific game. It’s like adding new rules to your favorite game to make it more fun!

Now, let’s talk about RAG. Picture having a friend who knows everything about anything. Seriously, they’re like a walking encyclopedia! RAG is kind of akin to having that friend inside your computer. It can ask this friend questions and get super-smart answers to use in its conversations. It’s akin to having a cheat code for chatting!

So, which one’s better? Well, it depends on what you need. If you want your computer to be a champ at a specific game (or task), fine-tuning is your go-to. But if you want it to be a walking encyclopedia, helping out with all sorts of questions, RAG is the way to go.

Personally, I think both are awesome! Fine-tuning lets us tailor-make our computer buddies, while RAG gives them superpowers to find answers to anything.

Difference between Retrieval Augmented Generation vs. fine Tuning LLM

Aspect	Retrieval Augmented Generation (RAG)	Fine-Tuning LLM
Approach	Combines retrieval with generation models	Fine-tunes pre-trained models
Training Data	Large corpora + knowledge bases	Large text corpora
Retrieval Component	Yes	No
Model Size	Leverages pre-trained large models	Uses pre-trained models
Adaptability	Adaptable across domains/tasks	Requires task-specific fine-tuning
Data Efficiency	More data-efficient due to retrieval	May need more task-specific data
Fine-Tuning Overhead	Potentially lower	Can be significant
Knowledge Incorporation	Integrates external knowledge	Relies on pre-existing knowledge
Task Performance	Influenced by retrieval effectiveness	Influenced by fine-tuning quality
Interpretability	Depends on retrieval mechanism	May be limited
Resource Requirements	Resources for training & retrieval	Resources for fine-tuning
Model Complexity	Complex, combining retrieval & generation	Complex due to fine-tuning

When to Choose Retrieval Augmented Generation (RAG)

1. Knowledge Seeker: If your project involves accessing external information from sources like databases or the web, Retrieval Augmented Generation (RAG) offers an advantage. It enables your AI to gather additional knowledge, enhancing its understanding and responses.
2. Versatility: RAG suits tasks with different topics or multiple simultaneous tasks. It easily adapts to various scenarios by fetching relevant information when needed.
3. Data Efficiency: When labeled data is limited, RAG can still perform effectively by combining existing data with fresh insights from its retrieval component.
4. Transparency: RAG provides transparent responses by referencing the sources it consulted, which enhances trust and understanding.
5. Resource Management: Although it requires maintenance for its retrieval component, investing in RAG’s knowledge sources can significantly enhance its performance.

When to Choose Fine-Tuning Large Language Models (LLM)

1. Task-Specific Performance: Fine-tuning Large Language Models (LLMs) benefits in achieving excellence in specific tasks or domains.
2. Data Availability: With abundant task-specific data, fine-tuning LLMs improves performance by learning from relevant information.
3. Simplicity: Fine-tuning LLMs minimizes complexity and overhead, making deployment straightforward.
4. Scalability: It allows for easy adaptation to evolving task requirements, ensuring continued effectiveness.
5. Limited Knowledge Requirements: If the task can be accomplished using the knowledge already encoded in pre-trained LLMs, fine-tuning is a suitable choice.

Conclusion

In this article, I have discussed the Retrieval Augmented Generation vs. fine-tuning LLM: If you have any doubts or queries, feel free to ask me in the comment section. I am here to help you.

All the Best for your Career!

Happy Learning!

FAQ

Thank YOU!

Explore more about Artificial Intelligence.

Though of the Day…

‘ It’s what you learn after you know it all that counts.’

– John Wooden