LLM internals explained — Tokens, Context, Embeddings & Output Flow | #5 ( AI Security Masterclass)

Description

In this episode of the AI Security Masterclass, we break down how Large Language Models actually work — without hype, buzzwords, or marketing fluff. You’ll learn the real mechanics behind LLM behavior, including how text becomes tokens, how context windows shape responses, how embeddings represent meaning, how vector databases support retrieval, and what happens from prompt to final output. This foundation is critical for anyone working in AI security, model risk, prompt injection defense, or secure AI system design. What we cover: • What tokens are and why they matter • Context window limits and security impact • How embeddings represent meaning • What vector databases actually do • Prompt → model → output lifecycle • Where failures and attack surfaces appear If you want to secure AI systems, you first need to understand how they truly operate under the hood. Part of the AI Security Masterclass series. #cybersecurity #aisecurity #infosec #cloudsecurity #cissp #aiarchitecture #cisspexam #onlinesecurity #cism #exam