Retrieval-Augmented Generation (RAG) using LangChain and Pinecone - The RAG Special Episode

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Talk #0: Introductions and Meetup Announcements
By Chris Fregly and Antje Barth, Generative AI Engineers @ AWS

Talk #1: Retrieval Augmented Generation (RAG) using Langchain: An Answer-based Approach to Language Generation (~25mins)
By Giuseppe Zappia, Sr Solutions Architect @ AWS

In this technical overview, we will explore the concept of Retrieval Augmented Generation (RAG) and its role in improving the outputs for large language models (LLMs) by incorporating external data to user prompts to create a high-quality, contextually relevant output. You'll learn about the components of RAG workflows and how to use the open source framework Langchain to reduce complexity while increasing development velocity for building GenAI applications.

Talk #2: RAG with Pinecone Vector Store (~25mins)
By Roie Schwaber-Cohen, Staff Developer Advocate @ Pinecone

Roie Schwaber-Cohen is a Staff Developer Advocate at Pinecone. He has an extensive background in software development and architecture with a specialization in large-scale applications and AI. A significant part of his work involves bridging the gap between the Python and TypeScript/JavaScript worlds in the field of AI. As a developer advocate, he regularly shares his work through his articles and demos for Pinecone.

== Related Links ==

Generative AI on AWS

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