CompTIA SecAI+ Domain 1.2: Tokens, Embeddings & Vector Databases Explained

Description

Computers don't speak English. They speak Math. In Part 2 of our CompTIA SecAI+ Deep Dive, we break down the "Data Pipeline." If you want to secure an AI model, you first need to understand how it translates chaotic human language into structured mathematical vectors. This video covers the most abstract (and critical) technical concepts in Domain 1. We explain exactly how Tokenization works, the magic of Embeddings (King - Man + Woman = Queen), and why Vector Databases are the backbone of modern RAG systems. We also cover critical exam topics like Context Window Overflows and the difference between Zero-Shot and Few-Shot prompting. 🎓 In this video, you will learn: Tokenization: Why 1,000 tokens ≈ 750 words, and why this limit matters for security. Embeddings: How AI maps words in a 3D space to understand meaning. Vector Databases: The difference between SQL and Semantic Search (RAG). Context Window: How attackers use "Short-Term Memory" limits to crash models (DoS). Prompt Engineering: The security implications of Zero-Shot vs. Few-Shot prompting. ⏱️ Timestamps: 00:00 Intro: The Math Problem 01:15 Tokenization & The 75% Rule 02:00 Embeddings & The "King - Man" Formula 03:10 Vector Databases & Semantic Search (RAG) 04:10 The Context Window & DoS Risks 04:35 Exam Tip: Zero-Shot vs Few-Shot Prompting 05:00 Support the Channel (Store & Podcast) 05:20 What's Next (Domain 1.3: Fine-Tuning) 🔗 Links & Resources: 👕 Official Sec Guy Store: https://sec-guy.printify.me/ 🎧 Sec Guy Podcast: 📺 Watch Part 1 (Neural Networks): https://youtu.be/VGYF5Dq6dKc 📜 Official CompTIA SecAI+ Objectives: https://www.comptia.org/en-us/certifications/secai #SecAIplus #CompTIA #Cybersecurity #AIsecurity #VectorDatabase #RAG #SecGuy #PromptEngineering