Introduction to LLMs

Introduction

A Large Language Model (LLM) is a neural network trained on enormous amounts of text to predict the next token (chunk of a word). That single objective — 'predict the next token' — turns out to be powerful enough to produce essays, code, translations, summaries and conversations.

Beginner analogy: Imagine the world's best autocomplete. You type a few words; it predicts the next one. Now scale that autocomplete to billions of parameters and the entire internet — you get ChatGPT.

Understanding the topic

Core concepts to understand:

• Trained on trillions of tokens of text from the web, books, code.
• Built on the transformer architecture (attention + feed-forward layers).
• Sizes range from 7B (small, runnable on laptop) to 1T+ (frontier models).
• Capabilities: chat, coding, summarisation, translation, reasoning.
• Limitations: hallucinations, knowledge cutoff, cost, latency.

Syntax reference

Visual workflow / architecture:

Prompt:  "The capital of France is"
                  │
                  ▼
        ┌──────────────────┐
        │  Tokenizer       │  → [The, capital, of, France, is]
        └────────┬─────────┘
                 ▼
        ┌──────────────────┐
        │  Transformer     │  ← billions of weights
        │  (many layers)   │
        └────────┬─────────┘
                 ▼
        Probability distribution over next token
        Paris: 92%   London: 3%   Berlin: 1% ...
                 ▼
              "Paris"

Real-world use

GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro and open-source Llama 3.1 405B are today's leading LLMs. They power Cursor, Perplexity, Copilot, ChatGPT, Notion AI and thousands of SaaS products.

Best practices

• Use the smallest model that works — cost & latency drop dramatically.
• Set a low temperature for factual tasks, higher for creative ones.
• Always have a fallback model in case your primary provider is down.

Common mistakes

• Treating LLM output as truth without verification.
• Ignoring token limits — too-long prompts get truncated silently.

Hands-on exercise

Interview preparation — practice these questions:

• Q1. What is the core training objective of an LLM?
• Q2. Difference between GPT-4 and Llama 3?
• Q3. Why are LLMs sometimes wrong (hallucinations)?
• Q4. What is a knowledge cutoff?