If you’ve ever used ChatGPT, Gemini, or Claude, you’ve already interacted with what’s called a Large Language Model (LLM).
But have you ever wondered — how do these models actually work?
How can they write essays, answer questions, or even generate code like humans?
Let’s break it down step by step — in simple language.
What is a Large Language Model (LLM)?
A Large Language Model is an AI system trained to understand and generate human-like text.
It doesn’t “know” things the way humans do — instead, it learns patterns in language from massive amounts of data (books, websites, articles, etc.) and predicts the next word in a sentence.
Example: If you say “The sky is…”,
the model predicts the next word might be “blue.”
That’s the basic idea — but at a very, very large scale.
Why “Large”?
Because these models are trained on huge datasets (terabytes of text) and have billions of parameters — the internal “knobs” the model adjusts while learning.
More parameters → deeper understanding of context, grammar, facts, and tone.
For example:
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GPT-2 → 1.5 billion parameters
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GPT-3 → 175 billion parameters
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GPT-4 → Estimated trillions of parameters
So, “large” refers to both the amount of data and the size of the neural network used to train the model.
The Core Concept: Prediction
At its heart, an LLM is just a next-word predictor.
It doesn’t store exact answers — it generates words one by one, based on probability.
Example: You type → “I love eating…”
The model calculates probabilities like:
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“pizza” → 0.65
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“ice cream” → 0.30
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“rocks” → 0.05
Then it picks “pizza,” because it’s most likely in context.
That’s how it builds sentences — predicting one token (word or sub-word) at a time.
The Secret Sauce: Transformers
The breakthrough behind modern LLMs is the Transformer architecture, introduced by Google in 2017 in a paper titled “Attention Is All You Need.”
Before Transformers, AI struggled to handle long sentences or remember earlier words. Transformers solved that with a concept called Self-Attention.
What Self-Attention Does?
It allows the model to “focus” on important words in a sentence — just like humans do.
Example: In “The cat that chased the mouse was tired,”
the model learns that “cat” and “was tired” are related — not “mouse.”
This attention mechanism gives LLMs their contextual understanding.
How LLMs Learn (Training Process)?
Here’s a simplified version of how these models are trained:
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Data Collection — Billions of sentences from books, Wikipedia, code, articles, etc.
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Tokenization — Breaking text into small pieces called “tokens” (like words).
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Training — The model learns to predict the next token by adjusting internal weights.
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Fine-Tuning — Additional training for specific tasks (like coding, chatting, summarizing).
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Reinforcement Learning (RLHF) — “Reinforcement Learning from Human Feedback” helps the model give better and safer answers.
After all this, you get a model that can write stories, debug code, summarize text, and even reason logically!
LLMs in Agentic AI:
Now, here’s where it gets exciting:
In Agentic AI, LLMs act as the “brain” of AI agents. They don’t just chat — they:
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Understand your goals
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Plan steps
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Use tools (like APIs or databases)
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And decide what action to take next
The more powerful the LLM, the smarter and more capable the agent.
Real-World Examples of LLMs:
Here are some of the popular models powering modern AI systems:
| Model | Company | Specialty |
|---|---|---|
| GPT-4 | OpenAI | Text, reasoning, coding |
| Gemini | Multimodal (text, image, video) | |
| Claude 3 | Anthropic | Safe and context-aware AI |
| LLaMA 3 | Meta | Open-source language model |
| Mistral | Mistral AI | Lightweight and efficient |
| Command R | Cohere | Retrieval-augmented generation |
Limitations of LLMs:
- Hallucination — Sometimes generate wrong or made-up facts.
- Dependence on training data — Can’t know anything beyond what they were trained on.
- High computational cost — Training and running large models require huge resources.
- Ethical concerns — Data bias, misinformation, and responsible AI usage.
✅ Final Thoughts:
Large Language Models are the foundation of modern AI — they give machines the ability to understand and communicate like humans. They’ve transformed how we code, write, learn, and even think about intelligence. But in the world of Agentic AI, they are just the starting point. An LLM is the brain, but it becomes truly powerful when combined with memory, tools, and autonomous reasoning — turning it into a full-fledged AI Agent.
As you continue your Agentic AI journey, remember:
“The LLM gives the AI its voice — but the Agent gives it purpose.”