Using Retrieval Augmented Generation to prevent hallucination and knowledge cut-offs in Large Language Models

In this session, Vikram will cover the history and evolution of LLMs starting with Transformers architecture and its applicability in a variety of Natural Language Processing tasks. He will then pivot to the introduction of LLMs, the different types of encoder-decoder paradigms and how LLMs have changed the ML game with recent mass adoption. Vikram will then discuss some of the Pros and Cons with LLMs specifically focusing on challenges with hallucinations and knowledge cut-offs. Vikram will discuss some options to mitigate these challenges and introduce Retrieval Augmented Generation, tokenization and the power of Vector DBs for semantic matching. For customers/organizations to quickly leverage the power of LLMs, Vikram will introduce Amazon Kendra, an ML powered search service and talk about why Amazon Kendra is mission-critical for organizations looking to adopt Generative AI within their enterprises securely, reliably, quickly, and realize business benefits at scale when integrating with these LLMs. Vikram will walk through key industry use cases for LLMs that a RAG approach can provide immediate benefits and discuss how customers/community can get started today to implement Kendra with LLMs on AWS.

Key Takeaways:

1. Introduction to NLP
   
2. Overview of Transformers
   
3. Evolution of Transformers to LLMs
   
4. Mitigation options for hallucination and knowledge cutoffs
   
5. Introducing Retrieval Augmented Generation
   
6. RAG at scale with Amazon Kendra
   
7. Industry use cases leveraging benefits of Amazon Kendra with LLMs on Amazon Bedrock
   
8. Getting started with Amazon Kendra and Amazon Bedrock today