Understanding Large Language Models

Learning Objectives

• Understand the fundamental concepts behind Large Language Models
• Grasp the key technical components of LLMs
• Learn about different types of LLMs and their applications
• Understand basic prompting and interaction patterns

1. What is an LLM?

Basic Definition

• A Large Language Model is a deep learning model trained on vast amounts of text data
• It learns patterns in language to predict the next most likely token (word/character) in a sequence
• Modern LLMs can understand and generate human-like text across multiple domains and tasks

Key Characteristics

• Transformer architecture-based
• Trained on hundreds of billions of tokens
• Uses attention mechanisms to understand context
• Can handle various tasks without task-specific training

2. Technical Foundation

Architecture Overview

• Based on the Transformer architecture (2017)
• Key components:
  • Self-attention layers
  • Feed-forward neural networks
  • Layer normalization
  • Positional encoding