Amazon Bedrock – RAG Explained with a Hands on Demo скачать в хорошем качестве

Amazon Bedrock – RAG Explained with a Hands on Demo

What if your AI could instantly pull the right information from your company’s own data? In this video, we’ll explore Retrieval-Augmented Generation (RAG) — what it is, why it matters, and how to set it up step-by-step inside Amazon Bedrock using a real AWS project dataset. ⏱️ Chapters 00:00 – Intro: What you’ll learn today 00:15 – What is RAG? (Retrieval-Augmented Generation) 01:05 – Why RAG is important for accurate, real-world answers 01:30 – When to use RAG in enterprise projects 01:52 – How RAG works step-by-step 02:15 – RAG hands-on overview 02:55 – Creating an IAM user for Bedrock 04:30 – Exploring the project dataset 05:30 – Uploading dataset to Amazon S3 06:10 – Creating a Knowledge Base in Amazon Bedrock 07:07 – Understanding data source options 09:07 – Choosing parsing strategies (Default, Data Automation, FM Parser) 10:30 – Chunking strategy explained (Default, Fixed, Semantic, etc.) 13:30 – Configuring embeddings and vector store 15:40 – Testing RAG setup in Bedrock console 17:10 – Cleanup and resource deletion steps 👩‍💻 If you found this useful, don’t forget to like, share, and subscribe to ImTechnos for more AI + AWS tutorials. 💬 Drop your questions in the comments — I’d love to help you out!