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When people talk about AI Agents or Agentic AI, one question often comes up:
“What actually makes an AI agent intelligent?”
The short answer is: Large Language Models (LLMs).
LLMs are often called the “brain” of modern AI agents — and for good reason. Let’s understand why in simple terms:
If you’ve ever wondered how ChatGPT, Gemini, or Claude can understand long sentences, follow context, and give human-like replies — the secret lies in a powerful deep learning architecture called the Transformer.
Let’s break it down step-by-step:
Back in my college days, most students were busy mastering C++ or Java, because every company visiting the campus preferred those languages for coding rounds.
Even my faculty advised me to pick Java and “at least become an expert in it.”
But I had already made my decision.
I chose Python — and decided to stick with it.
When we talk about AI tools like ChatGPT, Gemini, or Copilot, we often hear two terms used almost interchangeably —
Generative AI and Large Language Models (LLMs).
But are they the same thing?
Not exactly.
Let’s understand the difference — in simple language.
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.
If you’ve used ChatGPT, Gemini, or Claude, you’ve already interacted with what’s called a Large Language Model, or LLM.
But what exactly is an LLM?
And how is it different from regular AI models?
Let’s understand this concept.
If you’ve been following the rise of Artificial Intelligence, you might have heard new buzzwords like AI Agents and Agentic AI
These terms sound fancy — but what do they actually mean? Let’s break them down in simple words.
If you’ve been following the world of Artificial Intelligence lately, you’ve probably heard the term “Generative AI” everywhere — from ChatGPT to AI art to music generation.
But what exactly is Generative AI?
How is it different from traditional AI or Machine Learning?
Let’s break it down step by step in simple language.
When learning Machine Learning, two of the first terms you’ll encounter are Supervised Learning and Unsupervised Learning.
These are the two main ways machines learn from data — and understanding their difference is key to mastering AI.
If you’ve just started exploring AI and ML, you’ve probably come across terms like Statistical Learning and Deep Learning. They sound similar, but they actually represent two very different approaches to solving problems with data.
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