What is LLM?

By Ashwini • Nov 06, 2025

A Large Language Model (LLM) is a type of Artificial Intelligence that can understand, generate, and reason with human language — like ChatGPT, Gemini, Claude, or LLaMA.

It’s called “large” because it is trained on massive datasets (like text from books, websites, and code) and has billions or even trillions of parameters — the internal “knobs” that help it learn patterns in language.

🔹 Simple Explanation

Think of it like this:

🧠 An LLM is a digital brain that has read the entire internet
📚 It learns how words relate, how sentences are structured, and what ideas mean
💬 Then it uses this knowledge to predict the next word or sentence — that’s how it generates human-like text.

AI LLM

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Vineet

The core model architecture behind all modern Large Language Models (LLMs) is the

Transformer model, which was introduced in the 2017 paper "Attention Is All You Need". This architecture revolutionized natural language processing (NLP) by replacing sequential processing (used in previous models like Recurrent Neural Networks and LSTMs) with a self-attention mechanism that allows for parallel processing and a deeper understanding of context over long sequences of text.

Key Components of the Transformer Architecture

The Transformer architecture is built on a few key innovations:

Self-Attention Mechanism: This is the most critical component. It allows the model to weigh the importance of different words in an input sequence relative to each other, regardless of their position in the sentence. This helps the model understand nuance, context, and long-range dependencies in language.
Positional Encoding: Since the Transformer processes words in parallel rather than sequentially, positional encodings are added to the word embeddings to provide the model with information about the order and position of each word in the sequence.
Encoder-Decoder Structure: The original Transformer model consists of both an encoder and a decoder stack.
- The encoder processes the input sequence to build a rich contextual representation (embeddings).
- The decoder then uses this representation to generate the output sequence, predicting one word (or token) at a time in an autoregressive fashion

TechTree

What is LLM?

🔹 Simple Explanation

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