LLMs - Training vs Fine-Tuning vs RAG

These terms often confused me at the beginning, specially the distinction between fine-tuning and RAG was not very clear to me at first.

So here I share what I have learned so far. I hope it will help clarify any doubts you have!

Training an LLM

This is like schooling your robot from kindergarten.

Starting from scratch, this process takes a long time and require huge datasets like a ton of books and it also takes a lot of computing power.

For example, imagine you build a robot that doesn't know anything. You give it millions of books and let it read for months. After that, it knows a lot but still might not be perfect at everything.

Fine-Tuning

This is more like specialized training. Now your robot has finished school and is taking a vocational training course!

By fine-tuning, you can make your LLM really good at one specific thing. This involves taking an existing LLM and updating it with additional data so that it performs better in a specific area.

For example, a bank wants an AI chatbot to help customers with their banking questions, like "How do I open a savings account?" or "What are the loan interest rates?"

A fine-tuning solution would be like,

• The bank starts with a general-purpose LLM (like GPT).

• They fine-tune it using thousands of bank-specific FAQs, policies, and real customer support transcripts.

• Now, the chatbot understands banking terms better, avoids making general mistakes, and gives more accurate answers based on the bank's policies.

• Even without internet access, it can answer most questions correctly because it has "learned" from bank-specific data.

RAG (Retrieval-Augmented Generation)

Let's say you want to specialize your LLM for financial market analysis.

You'll have to fine-tune it with up-to-date stock market insights, trends, and forecasts. These data updates everyday, every minute, how practical is it to fine-tune a model that frequently? It's not practical at all!

That's when RAG comes in to picture.

Instead of updating the LLM with latest data all the time, we let the LLM access updated data and come up with answers.

RAG also makes the LLM specialized in one thing, but this time, the LLM is not fed with specialized knowledge like before. The specialized data sits separately, and the LLM fetches the data and uses it to answer questions.

Let's look at a RAG example:

You wan to build an AI shopping assistant for an E-Commerce store.

An online store sells thousands of products, and their inventory, prices, and descriptions keep changing. A chatbot needs to help customers find the right products, but fine-tuning wouldn’t work well because the data updates daily.

Solution: Using RAG

1. User Question: A customer asks, “Do you have any wireless headphones under $100?”

2. Retrieval Step: The AI searches the product database for wireless headphones priced under $100.

3. Generation Step: The AI formats the response:

   • "Yes! Here are three wireless headphones under $100:"

   • Brand A – $89.99 (20-hour battery life, noise cancellation)

   • Brand B – $79.99 (Bluetooth 5.2, water-resistant)

   • Brand C – $99.99 (Great sound quality, fast charging)

4. Dynamic Updates: If a product is out of stock or a price changes, the AI automatically fetches the latest data without needing retraining.

Fine-Tuning vs. RAG – When to Choose Which?

Fine-Tuning + RAG

Yes, you could fine-tune the model to have a specific brand voice and customer-friendly responses while using RAG to fetch live product data.

• Fine-Tuning: Teaches the AI how to sound like your brand (e.g., friendly, luxury, or tech-savvy).

• RAG: Makes sure it always provides fresh, accurate product information.

What is your experience with Fine-Tuning or RAG? Which one have you used? I’m curious, hit reply and let’s chat.

Have a great week!