1. Introduction to LLMs and Prompt Engineering

• The video focuses on LLMs and Prompt Engineering, both crucial for anyone aspiring to work in Generative AI.
• It suggests prior knowledge of Machine Learning, Deep Learning, Neural Networks, and Transformers to fully grasp the content.
• Think of an LLM as a person who has read the entire internet—they don’t remember everything but have learned the patterns in how people speak and write. When prompted, they generate new responses based on these patterns, not by recalling facts.
• Technically, an LLM is a neural network trained on large-scale text datasets (e.g., books, articles, code, tweets).
• LLMs do not store or understand facts like a database. Instead, they predict the next token based on learned probabilities and patterns.
• They can hallucinate, especially with current events or complex math. However, they are effective in tasks such as rewriting, summarizing, or translating text.
• LLMs are evolving into interface layers for software, integrating with tools like Notion, Excel, or databases.

2. How LLMs Work Internally

LLMs operate through two key components:

a. Tokenization

• LLMs process numbers, not words.
• Each word is converted into a token—a numerical identifier (e.g., “Chat” = 1003).
• Tokens are then represented as vectors.
• Token limits are critical: more tokens increase cost, memory, and compute time—one reason most LLMs are paid.

b. Transformers

• Based on the paper “Attention Is All You Need”.
• Transformers process entire input sequences at once using a self-attention mechanism to weigh contextual importance.
• For example, in “The trophy didn’t fit in the suitcase because it was too big,” the model identifies “it” as referring to “trophy” using past context.