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Build a question-answering bot using generative AI

Offered By: Amazon Web Services via AWS Skill Builder

Tags

Generative AI Courses Amazon SageMaker Courses Amazon Lex Courses Data Engineering Courses Amazon Kendra Courses Retrieval Augmented Generation (RAG) Courses

Course Description

Overview

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This lab demonstrates to a how to build a question-answering chatbot that uses stateless, retrieval augmented generation to provide answers to your questions about AWS Classroom courses.


Objectives:


By the end of this lab, you should be able to do the following:

  • Explain how retrieval augmented generation can be used to improve the output produced by Generative AI applications.
  • Deploy a Lex chatbot powered by a large language model.
  • Connect Langchain to a model launched in Amazon SageMaker.


Prerequisites:


To complete this lab it is recommended that you have a technical understanding of:

  • Amazon SageMaker
  • Amazon Kendra
  • Amazon Lex

Being familiar with FLAN and LLMs will be a benefit


Audience:


  • Solutions Architect
  • Data Engineer
  • Data Scientist
  • Developer


Outline:


Task 1: Deploy a large language model (LLM)
Task 2: Add an Amazon Kendra data source
Task 3: Create an Amazon Lex V2 chatbot
Task 4: Query your large language model endpoint
Task 5: Implement a RAG workflow
Task 6: Deploy a web app with Cloudformation


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