What Is an LLM and How Does It Work?

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Introduction

Large Language Models (LLMs) such as GPT, Claude, and Gemini have changed how software is built. They write code, answer questions, and reason about complex topics. But behind the scenes, LLMs are still machines that operate on math, probabilities, and data.

This article explains how LLMs work in simple engineering terms.

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What Is a Large Language Model?

An LLM is a neural network trained on massive amounts of text data to predict the next token in a sequence.

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Given: "The sky is"  
Predict: "blue"

This ability to predict text allows the model to generate paragraphs, write programs, and hold conversations.

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How LLMs Are Trained

Training an LLM involves feeding billions of text examples into a neural network and adjusting its parameters so that its predictions become increasingly accurate.

The same training loop applies:

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Input → Prediction → Error → Correction → Repeat

Over time, the model internalizes grammar, facts, reasoning patterns, and knowledge.

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Transformers and Attention

LLMs are built on the Transformer architecture. The core innovation is self-attention.

Attention allows the model to weigh the importance of each word relative to every other word in a sentence.

This lets the model understand context, meaning, and relationships between words.

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Tokens and Probabilities

LLMs do not "know" words. They process tokens.

At every step, the model calculates probabilities for the next possible token and selects one based on those probabilities.

This is why LLMs sometimes make mistakes — they generate what is statistically likely, not what is guaranteed to be true.

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Where Embeddings Fit

Before text is processed, tokens are converted into embeddings. These embeddings encode semantic meaning and are the input to the transformer network.

This connects language understanding directly to vector mathematics.

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Why LLMs Are So Powerful

Because LLMs combine:

• embeddings for understanding
• attention for context
• massive data exposure
• enormous neural networks

they can generalize across programming, writing, mathematics, and reasoning.

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Final Takeaway

LLMs are not magic. They are mathematical machines that learned language by observing the world at scale.

Understanding LLMs allows engineers to build safer, more powerful, and more reliable AI systems.