What Are AI Model Parameters? Let Me Explain
My friend asked me yesterday, "What does it mean when they say GPT-4 has 1.7 trillion parameters? What even are parameters?"
Great question! I realized a lot of people hear these huge numbers 175 billion, 1.7 trillion and have no idea what they actually represent. So let me explain it the way I explained it to my friend, using something we both understand: cooking.
Let's Start With What We Know
When you hear about AI models, you'll see numbers like:
- GPT-3 has 175 billion parameters
- GPT-4 has around 1.7 trillion parameters
- Claude 3.5 Sonnet has roughly 400 billion parameters
These numbers are huge. But what do they mean? Are they storing that many facts? That many sentences? Let me break it down.
Think About a Chef
Imagine you're learning to cook. You start with recipes, ingredients, and lots of practice. Over time, you don't just follow recipes anymore you understand cooking. You know when to add more salt, how long to cook something, which spices work together.
AI models work the same way.
The Raw Ingredients = Training Data
When we train an AI model, we feed it massive amounts of text books, websites, conversations, code, articles. Think of this as the raw ingredients. Just having flour, spices, and vegetables doesn't make you a good cook. You need to learn how to use them.
The Chef's Skill = Parameters
Here's where parameters come in.
Parameters are not the training data. They're what the model learned from that data. Think of them as the chef's skill, experience, and intuition.
When a chef cooks biryani 1,000 times, they learn:
- Exactly how much salt balances the rice
- When to add the spices for maximum flavour
- How long to cook it based on the heat
- How to adjust if something goes wrong
They didn't memorize 1,000 biryani recipes. They developed an understanding of how biryani works. That understanding those tiny adjustments and decisions stored in their mind that's what parameters are in AI.
How Does the Learning Actually Happen?
This is the most important part that many explanations skip.
Imagine a student chef learning to make biryani. Here's what happens:
Step 1: They cook the biryani (using their current knowledge)
Step 2: The master chef tastes it and says, "Too much salt" or "Not enough spice"
Step 3: The student adjusts their technique maybe they use half a teaspoon less salt next time, or add cardamom earlier
Step 4: They cook again with these adjustments
Step 5: Repeat this thousands of times
After 1,000 attempts, the student doesn't need the master chef anymore. They've internalized the patterns. They know instinctively how to make great biryani.
This Is Exactly How AI Training Works
The AI model reads billions of sentences from its training data. For each sentence, it:
- Tries to predict the next word — "The cat sat on the ___"
- Checks if it was right — the actual word was "mat"
- Adjusts its internal numbers (parameters) to make better predictions next time
- Repeats this billions of times across all the text
Through this process, the model isn't memorizing sentences. It's learning patterns:
- Grammar rules (subjects come before verbs)
- Word relationships (cats sit, birds fly)
- Context (a river "bank" vs. a money "bank")
- Reasoning patterns (cause and effect)
By the end of training, those 1.7 trillion parameters contain all these learned patterns. They're like the compressed wisdom the model gained from reading all that text.
So What Does "1.7 Trillion Parameters" Actually Mean?
When we say GPT-4 has 1.7 trillion parameters, we're saying it has 1.7 trillion tiny adjustable numbers that store all this learned knowledge.
Each parameter is like a single tiny piece of knowledge:
- "When this word appears, slightly increase the chance of that word coming next"
- "In this context, this phrase structure is more likely"
- "These concepts are related in this way"
More parameters = more capacity to store subtle patterns and nuances. It's why larger models can often understand context better and give more sophisticated responses.
But here's the key: more parameters doesn't mean more facts memorized. It means more ability to understand the patterns in language.
When You Ask ChatGPT a Question
Now when you type a question into ChatGPT, here's what happens:
You're like a customer ordering food. The AI chef doesn't look up your exact question in a database. Instead, it uses all those 1.7 trillion learned patterns (parameters) to generate a fresh response, right there on the spot.
That's why it can answer questions it has never seen before. Just like a skilled chef can create a new dish without having the exact recipe, the AI can create new answers using the patterns it learned.
Why Smaller Models Can Still Be Good
You might wonder: if more parameters are better, why do we have smaller models?
Think about it this way. You don't need a Michelin star chef to make good everyday food. Sometimes a home cook with good fundamentals can make an excellent meal.
Newer models like GPT-4o (around 200 billion parameters) are designed smarter. They might have fewer parameters, but they're organized more efficiently. They can still perform really well for most tasks while being:
- Faster to respond
- Cheaper to run
- Easier to use on different devices
The Simple Truth
So when someone asks you what AI parameters are, tell them this:
Parameters are the learned knowledge stored inside the AI model. They're created through billions of training examples, where the model keeps adjusting itself to make better predictions. They're not memorized facts they're patterns and relationships the model discovered in language.
It's like the difference between someone who memorized a cookbook and a chef who understands why ingredients work together. The AI has 1.7 trillion tiny pieces of understanding that help it generate intelligent responses to questions it has never seen before.
That's it. That's what parameters are.
But Wait What About RAG and Fine-tuning?
Now here's where it gets even more interesting. My friend then asked, "But what about when people talk about RAG or fine-tuning? How does that fit in?"
Great question! Let me extend our cooking analogy.
LLMs Are Like Frozen Food
Think of a trained LLM (like GPT-4 or Claude) as high quality frozen food. It's already prepared, cooked, and ready. The chef (the company that trained it) has already done the hard work. All those parameters? They're frozen locked in place.
But you can still make it better or customize it for your needs. Here are two ways:
RAG (Retrieval Augmented Generation) = Adding Fresh Ingredients
Imagine you have frozen biryani. It's good, but you want to make it your own. So you:
- Heat it up
- Add fresh coriander on top
- Mix in some raita
- Maybe add extra fried onions
You didn't change the frozen biryani itself. You just added fresh ingredients around it to make it better and more customized.
This is exactly what RAG does.
When you use RAG, you're not changing the AI's parameters (the frozen food stays frozen). Instead, you're giving it fresh, relevant information right when it needs it:
- You ask: "What did our company discuss in last week's meeting?"
- RAG system searches your company documents
- It finds the meeting notes
- It gives those notes to the AI along with your question
- The AI uses its frozen knowledge (parameters) + the fresh information (meeting notes) to answer
The base model stays the same, but you've enhanced it with up-to-date, specific information. Just like adding fresh ingredients to frozen food.
Fine-tuning = Making a New Dish From Frozen Food
Now imagine something different. You take that frozen biryani and decide to completely remake it:
- You add paneer and make it paneer biryani
- Or add extra vegetables and spices to create a completely new flavour profile
- You're essentially creating a new dish using the frozen food as a base
This is fine-tuning.
When you fine tune an AI model, you're actually unfreezing some of those parameters and training them again on your specific data:
- You start with the base model (frozen food)
- You train it on your specific examples (adding new ingredients and cooking it differently)
- The parameters adjust to your specific use case
- You end up with a customized model
For example, a hospital might fine tune GPT-4 on medical records to create a specialized medical AI. The base knowledge is still there (language patterns, reasoning), but now it's been adjusted to understand medical terminology and patterns better.
The Key Difference
RAG = Keep the model frozen, just add fresh information when needed
- Fast to set up
- No need to retrain anything
- Perfect for adding new, changing information
Fine-tuning = Unfreeze and adjust the model itself
- Takes more time and resources
- Changes the actual parameters
- Perfect for specialized tasks or domain specific knowledge
Both approaches use the same idea: the pre trained model (with its trillions of parameters) is your starting point your frozen food. But depending on what you need, you either add fresh ingredients around it (RAG) or transform it into something new (fine tuning).
Note: The exact parameter counts for newer models are often estimates, as companies don't always publish official numbers. But the concept remains the same parameters represent the learned patterns, not the raw data.
Thanks
Sreeni Ramadorai
Top comments (4)
Hi sir,
Thanks for this wonderful article that explains the key concepts of RAG, AI models parameters and fine-tuning in a lucid manner. I like the way you explained it with the cooking analogy.
Keep helping us.
Thanks and regards,
Praveen
Thanks you Praveen
This is a really clear way to explain AI parameters! Think of it like a chef learning to cook: the model’s parameters are like all the subtle skills and instincts a chef develops after practicing thousands of times. They don’t memorize every recipe—they learn patterns and rules. For AI, those 1.7 trillion numbers capture language patterns, grammar, and reasoning. When you ask a question, the model isn’t recalling a fact—it’s using those learned patterns to generate a response, kind of like a chef improvising a dish.
Thank you Sehag.