LLM Introduction

Introduction

An LLM (Large Language Model) is a transformer-based neural network trained on internet-scale text to predict the next token. The 'large' refers to billions of parameters and trillions of training tokens. That scale is what unlocks reasoning, coding and general-purpose chat.

Beginner analogy: Imagine the world's most well-read autocomplete. Now scale it to read the whole internet — that's an LLM.

Understanding the topic

Core concepts to understand:

• Predicts next token, repeatedly, to produce text.
• Sizes range from 7B (laptop) to 1T+ (frontier).
• Capabilities scale roughly with parameters × training tokens (Chinchilla scaling laws).
• Costs scale linearly with input + output tokens.

Syntax reference

Visual workflow / architecture:

Prompt → Tokens → Transformer → Next-token probs → Sample → repeat
                                                              │
                                                              ▼
                                                       Final text

Real-world use

GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro, Llama 3.1 405B, Mistral Large, Qwen 2.5 72B are today's leading LLMs. They power most modern AI products.

Best practices

• Pick the smallest model that meets quality needs.
• Always benchmark cost, latency, accuracy together.

Hands-on exercise

Interview preparation — practice these questions:

• Q1. What is an LLM in one sentence?
• Q2. Why does scale matter?
• Q3. Name three frontier LLMs.