Lab - Explore Generative AI Use Cases with LangChain and Amazon Bedrock

Overview

In this lab, you step through Jupyter notebooks and make API calls to generative artificial intelligence (generative AI) models that are hosted in Amazon Bedrock. The notebooks use Bedrock to generate and summarize text, answer questions, and build a chatbot.

Syllabus

• Introduction to Generative AI
Overview of Generative AI concepts
Introduction to LangChain and Amazon Bedrock
• Setting Up the Environment
Jupyter Notebook installation and setup
Connecting to Amazon Bedrock
• Text Generation with Amazon Bedrock
Introduction to text generation models
API calls to generate creative text content
Practical exercises in Jupyter Notebook
• Text Summarization Techniques
Overview of text summarization methods
Implementing summarization using Amazon Bedrock
Hands-on exercises to summarize documents
• Question Answering with LangChain
Introduction to question-answering models
Formulating and sending queries
Real-world exercise: Providing accurate answers to sample questions
• Building a Chatbot
Basics of chatbot design
Integrating generative AI models with chat interfaces
Developing a simple chatbot in Jupyter Notebook
• Review and Assessment
Recap of key learnings
Project: Create a unique application using LangChain and Amazon Bedrock
Feedback and QA session
• Conclusion and Next Steps
Summary of course outcomes
Resources for further learning in Generative AI and LangChain
Course completion and certification process

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Subjects

Programming