Abstract: ReAct is an approach that uses human reasoning traces to create action plans which determine the best action to take from a selection of available tools that are external to the LLM. This methodology mimics human chain of thought processes combined with the ability to engage with an external environment to solve problems and reduce the likelihood of hallucinations and reasoning errors.
In this workshop, you will learn how to employ the ReAct technique to allow an LLM to determine where to find information to service different types of user queries, using LangChain to orchestrate the process. You’ll see how to it uses Retrieval Augmented Generation (RAG) to answer questions based on external data, as well as other tools for performing more specialized tasks to enrich the output of your LLM.
All demo code and presentation material will be provided, as well as a temporary Amazon SageMaker Studio environment to build and deploy in.
Module 1: Overview of Retrieval Augmented Generation (RAG)
Module 2: Introduction to ReAct, and LangChain
Module 3: Building a ReAct workflow with LangChain
Prerequisite and Background Knowledge Needed:
Basic Python knowledge
Basic GenAI/ML Understanding
Bio: Shelbee Eigenbrode is a Principal Machine Learning Specialist Solutions Architect at Amazon Web Services (AWS). She’s been in technology for 24 years spanning multiple roles, industries and technologies. With over 35 patents granted across various technology domains, she has a passion for continuous innovation and using data to drive business outcomes. She’s a published author as well as co-creator/instructor for ‘Practice Data Science on the AWS Cloud Specialization’ and ‘Generative AI with Large Language Models’ on Coursera. Shelbee is based in Denver, CO and is also the Co-Director of the Denver, Colorado chapter of Women in Big Data.