Generative AI on AWS - Amazon Bedrock, RAG & Langchain[2026]

• Learn fundamentals about AI, Machine Learning and Artificial Neural Networks.
• Learn how Generative AI works and deep dive into Foundation Models.
• Amazon Bedrock – Detailed Console Walkthough, Bedrock Architecture, Pricing and Inference Parameters.
• Use Case 1: Text Summarization for Manufacturing Industry using API Gateway, S3 and Cohere Foundation Model
• Use Case 2 - Build a Chatbot using Bedrock Converse API - DeepSeek and Nova Pro Foundation Model, Langchain and Streamlit
• Use Case 3- Employee HR Q & A App with Retrieval Augmented Generation (RAG) - Bedrock - Claude Foundation Model + Langchain + FAISS + Streamlit
• Use Case 4 : Serverless e-Learning App using Bedrock Knowledge Base + Claude FM + AWS Lambda + API Gateway
• Use Case 5 : Build a Retail Banking Agent using Amazon Bedrock Agents & Knowledge Bases
• Use Case 6 - Build Infrastructure Coding Agent using Amazon Q CLI and AWS CloudFormation Server.
• Use Case 7 : Amazon Q Business - Build a Marketing Manager App with Amazon Q
• Python Basics Refresher
• AWS Lambda and API Gateway Refresher

Amazon Bedrock, Amazon Q and AWS GenAI Course :

***Hands - On Use Cases implemented as part of this course***

Use Case 1 - Text Summarization for Manufacturing Industry using API Gateway, S3 and Cohere Foundation Model

Use Case 2 - Build a Chatbot using Amazon Bedrock - DeepSeek, Langchain and Streamlit.

Use Case 3- Build an Employee HR Q & A Application with Retrieval Augmented Generation (RAG) -

Claude FM + Langchain (Ochestrator)+ FAISS (Vector DB)+ Streamlit

Use Case 4 - Serverless e-Learning App using Bedrock Knowledge Base + Claude FM + AWS Lambda + API Gateway

Use Case 5 - Build a Retail Banking Agent using Amazon Bedrock Agents and Knowledge Bases -

Claude Sonnet + AWS Lambda + DynamoDB + Bedrock Agents + Knowledge Bases + OpenAPI Schema

Use Case 6 - Build Infrastructure CodingAgent using Amazon Q CLI and AWS CloudFormationServer.

Use Case 7 - Amazon Q Business - Build a Marketing Manager App with Amazon Q Business

• Welcome to the most comprehensive guide on Amazon Bedrock and Generative AI on AWS from a practising AWS Solution Architect and best-selling Udemy Instructor.

• This course will start from absolute basics on AI/ML, Generative AI and Amazon Bedrock and teach you how to build end to end enterprise apps on Image Generation using Stability Diffusion Foundation, Text Summarization using Cohere, Chatbot using Llama 2,Langchain, Streamlit and Code Generation using Amazon CodeWhisperer.

• The focus of this course is to help you switch careers and move into lucrative Generative AI roles.

• There are no course pre-requisites for this course except basic AWS Knowledge. I will provide basic overview of AI/ML concepts and have included Python, AWS Lambda and API Gateway refresher at end of course in case you are not familiar with python coding or these AWS services.

• I will continue to update this course as the GenAI and Bedrock evolves to give you a detailed understanding and learning required in enterprise context, so that you are ready to switch careers.

  
  

  Detailed Course Overview

• Section 2 - Evolution of Generative AI: Learn fundamentals about AI, Machine Learning and Artificial Neural Networks (Layers, Weights & Bias).

• Section 3 - Generative AI & Foundation Models Concepts: Learn about How Generative AI works (Prompt, Inference, Completion, Context Window etc.) & Detailed Walkthrough of Foundation Model working.

• Section 4 - Amazon Bedrock – Deep Dive: Do detailed Console Walkthough, Bedrock Architecture, Pricing and Inference Parameters.

• Section 5 - Use Case 1: Text Summarization for Manufacturing Industry using API Gateway, S3 and Cohere Foundation Model

• Section 6 - Use Case 2 : Build a Chatbot using Bedrock - DeepSeek, Langchain and Streamlit

• Section 7 - Use Case 3- Build a Employee HR Q & A Application with Retrieval Augmented Generation (RAG) -

  Amazon Bedrock (Claude Foundation Model) + Langchain (Ochestrator)+ FAISS (Vector DB)+ Streamlit

• Section 8 - Serverless e-Learning App using Bedrock Knowledge Base + Claude FM + AWS Lambda + API Gateway

• Section 9 - Build a Retail Banking Agent using Amazon Bedrock Agents and Knowledge Bases, Dynam0DB, Lambda

• Section 10 - Python Basics Refresher

• Section 11 - AWS Lambda Refresher

• Section 12 - AWS API Gateway Refresher

IMPORTANT >

Many learners ask how to switch their career to an AWS Generative AI Developer or Architect and which sequence of my Udemy courses they should follow. Here is some guidance based on my experience working in the IT industry.

My GenAI/Agentic AI courses are divided into two tracks

• Hands-On learning to build real world skills required in the IT industry (Most important)

• Certification preparation to help you pass the certification exam (Good to have)

>

1. Hands-On Course 1 (Beginner) - Amazon Bedrock, Amazon Q & AWS Generative AI [Hands-On]

Start here if you’re new to GenAI & AmazonBedrock.

2. Hands-On Course 2 (Intermediate) - Build Production Ready AI Agents on AWS – Bedrock, CrewAI & MCP

Take this after Course 1 - Focused on Agentic AI but will be easier to understand if you have taken Course 1

3. Hands-On Course 3 (Advanced) - Amazon Bedrock AgentCore : Deploy AI Agents on AWS

This is the advanced course and focused on how to deploy, scale, and operate AI agents in Production.

Recommend to take after Course 1 & Course 2.

>

1. Certification Course 1 : AWS Certified AI Practitioner (AIF-C01) – Beginner to Advanced

· Take after Step 1, or

· In parallel with Step 2

Outcome
You pass AWS Certified AI Practitioner (AIF-C01) and understand GenAI concepts AWS expects.

2. Certification Course 2 : AWS Certified Generative AI Developer Professional (ComingSoon)