RAG Explained | Amazon Bedrock Knowledge Bases & Vector Databases (Lesson 8)

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: 00:41 The Problem: Foundation Models are "Blind" to Private Data 01:09 What is RAG? (Retrieval Augmented Generation) 01:21 EXAM TIP: RAG vs. Fine-Tuning 02:11 The RAG Workflow: Step-by-Step Example 03:07 The 3 Building Blocks of Bedrock Knowledge Bases 03:54 Data Sources: Why Amazon S3 is King 04:00 04:15 Embeddings Models: Turning Text into Numbers 04:41 BIG EXAM TIP: Choosing the Right Vector Database 05:31 Real-World Use Cases: Customer Service, Legal, & Healthcare 06:05 Summary: The 3 Building Blocks Recap AWS AI Practitioner, RAG explained, Retrieval Augmented Generation, Amazon Bedrock, Bedrock Knowledge Base, AWS Generative AI, AWS AI exam prep, RAG on AWS, Vector databases AWS, Amazon OpenSearch RAG, Neptune Analytics Graph RAG, Amazon S3 Vectors, Titan embeddings, AWS embeddings model, AWS Bedrock tutorial, Generative AI AWS, AWS AI certification, Bedrock RAG architecture, RAG vs fine tuning, Knowledge Base Amazon Bedrock, AWS machine learning exam Find me here LinkedIn - https://www.linkedin.com/in/girish-mukim/ Website - https://imaginetechverse.com/ Twitter - https://twitter.com/GirishMukim YouTube - https://www.youtube.com/@AWSLearn