Gen AI Foundations: Tokenization, Context Windows, and Embeddings Explained (Lesson 9)

In this video, we break down the three most important theoretical concepts in Generative AI: Tokenization, Context Windows, and Embeddings. Whether you are preparing for a technical exam or building your first AI application, understanding how a model "reads" and "remembers" data is essential.

In this video, you will learn:

Tokenization: How raw text is converted into IDs (using the example: "Large language models don’t understand text — they understand tokens").

Context Windows: Why the "memory" of models like GPT-4, Claude, and Gemini 1.5 Pro matters for your use case.

Embeddings: How we turn words into high-dimensional vectors to capture meaning and sentiment for RAG and vector databases.

Your foundation model is incredibly smart…
but it has no idea who works at your company or where your internal data lives.
That’s the exact problem Retrieval Augmented Generation (RAG) solves and in this video, you’ll finally understand how RAG works inside Amazon Bedrock, step by step, in a way that actually sticks.

If you’re preparing for the AWS AI Practitioner certification, this is one of those topics you must get right. But more importantly, this is how real companies make AI useful with private, secure data.

In this video, we break down:
What RAG (Retrieval Augmented Generation) really means in plain English
Why foundation models cannot access private company data on their own
How Amazon Bedrock Knowledge Bases work end-to-end
The full RAG flow from user question → retrieval → augmented prompt → answer

The 3 core building blocks you will see on the exam:
Data sources (especially Amazon S3)
Embeddings models (like Amazon Titan)
Vector databases (OpenSearch, Neptune Analytics, S3 Vectors, Aurora)
EXAM TRIGGER WORDS that tell you which vector database to choose
Real-world RAG use cases in customer support, legal, and healthcare

This isn’t just theory. You’ll walk away knowing how AWS expects you to think when answering RAG questions on the exam and how this architecture is actually used in production.

If you’re watching multiple videos on:

AWS AI Practitioner
Amazon Bedrock
Generative AI on AWS
RAG vs fine-tuning 👉 this one connects all the dots.

Subscribe for more exam-focused AWS + Generative AI breakdowns that explain why, not just what.

📌 Timestamps:

00:33 The 3 Building Blocks of AI Understanding
00:41 Tokenization: The AI's "Alphabet"
01:21 How Tokenizers Handle Punctuation and Complex Words
02:08 Context Window: The AI's "Short-Term Memory"
02:44 The Race for the Largest Context Window
03:52 The Cost/Performance Trade-off of Large Windows
04:15 Embeddings: The AI's "Understanding"
05:07 Why Vectors Capture Nuance, Meaning, and Sentiment
05:27 Mathematical Distance: Why "Puppy" is near "Dog" 05:51
06:21 Final Summary & Exam Cheat Sheet

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