Generative AI on AWS - Amazon Bedrock, RAG & Langchain[2025]

About Generative AI on AWS - Amazon Bedrock, RAG & Langchain[2025]

_Amazon Bedrock, Amazon Q and AWS GenAI Course :_

_

Hands - On Use Cases implemented as part of this course

_

_Use Case 1_

_-_

Generate Poster Design

for Media Industry using API Gateway, S3 and

Stable Diffusion

Foundation Model

_Use Case 2_

_-_

Text Summarization

for Manufacturing Industry using API Gateway, S3 and

Cohere Foundation Model

_Use Case 3_

_-_

_Build a Chatbot_

_using Amazon Bedrock - DeepSeek, Langchain and Streamlit._

_Use Case 4_

_-_

_Build an Employee HR Q & A Application with Retrieval Augmented Generation (RAG) - _

_Claude FM + Langchain (Ochestrator)+ FAISS (Vector DB) + Streamlit_

Use Case 5 - Serverless e-Learning App

using

Bedrock

_Knowledge Base_

_+ Claude FM_

_+ AWS Lambda + API Gateway_

Use Case 6 - 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 7 - Build Infrastructure Coding Agent using Amazon Q CLI and AWS CloudFormation Server.

Use Case 8 - Amazon Q Business - Build a Marketing Manager App with Amazon Q Business

Use Case 9 - Amazon Q Developer - Overview of the Code Generation capabilities of Amazon Q Developer - Over the SDLC

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:

Media and Entertainment Industry: Generate Movie Poster Design using API Gateway, S3 and Stable Diffusion Foundation Model

Section 6 - Use Case 2:

Text Summarization for Manufacturing Industry using API Gateway, S3 and Cohere Foundation Model

Section 7 - Use Case 3 :

Build a Chatbot using Bedrock - DeepSeek, Langchain and Streamlit

_Section 8 - Use Case 4- Build a Employee HR Q & A Application with Retrieval Augmented Generation (RAG) - _

_Section 9 -_ Serverless e-Learning App

using

Bedrock

_Knowledge Base_

_+ Claude FM + AWS Lambda + API Gateway_

_Section 10 -_ Build a Retail Banking Agent

using Amazon Bedrock Agents and Knowledge Bases, Dynam0DB, Lambda

Section 11 - GenAI Project Lifecycle: Phase 1

- Use Case Selection - Discuss about various phases of GenAI and How to identify right use case

Section 12 - GenAI Project Lifecycle: Phase 2

- Foundation Model Selection - Theory and Handson using AWS Bedrock Model Evaluation Service

Section 13 - GenAI Project Lifecycle:

Phase 3 - Prompt Engineering - Factors Impacting Prompt design, Prompt design Techniques (Zero Shot, One Shot.), Good practices for writing prompts for Claude, Titan and Stability AI Foundation Models

Section 14 - GenAI Project Lifecycle: Phase 4

- Fine Tuning of Foundation Models - Theory and Hands-On

Section 15 - Code Generation using AWS CodeWhisperer

and CDK - In Typescript

Section 16

- Python Basics Refresher

Section 17 - AWS Lambda

Refresher

Section 18 - AWS API Gateway

Refresher

Services Used in the Course :

1. Amazon Bedrock 2. AWS CloudFormation MCP Server and Q CLI 3. Deepseek and Nova Pro Foundation Model 4. Cohere Foundation Model 5. Stability Diffusion Model 6. Claude Foundation Model from Anthropic 7. Claude Sonnet 8. Amazon Bedrock Agents 9. Bedrock Knowledge Base 10. Langchain - Chains and Memory Modules 11. FAISS Vector Store 12. AWS Code Generation using AWS Code Whisperer 13. API Gateway 14. AWS Lambda 15. AWS DynamoDB 16. Open API Schema 17. Streamlit 18. S3 19. Prompt design Techniques (Zero Shot, One Shot.) for Claude, Titan and Stability AI Foundation Models (LLMs) 20. Fine Tuning Foundation Models - Theory and Hands-On 21. Python 22. Evaluation of Foundation Models - Theory and Hands-On 23. Basics of AI, ML, Artificial Neural Networks 24. Basics of Generative AI 25. Everything related to AWS Amazon Bedrock