10:25 Hareesh Singireddy, Senior Solutions Architect, AWS

Generative AI for Every Organization: Strategic Overview

Learn how to boost productivity, build differentiated experiences, and innovate faster with AWS, leveraging its enterprise-grade security and privacy, access to industry-leading foundation and large language models (LLM), and generative AI-powered applications. Specific topics to be discussed include:

• Build and scale Generative AI applications with security, privacy, and responsible AI built in from day one

• Train and run inference at scale with infrastructure purpose-built for machine learning

• Use AWS’ powerful and easily customized applications/capabilities to boost productivity, streamline coding, simplify business intelligence, and seamlessly integrate multiple data sources

Amazon Bedrock, the Easiest Way to Build and Scale Generative AI Applications with Foundation Models

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI.

Using Amazon Bedrock, you can easily experiment with and evaluate top FMs for your use case, privately customize them with your data using techniques such as fine-tuning and Retrieval Augmented Generation (RAG), and build agents that execute tasks using your enterprise systems and data sources.

Since Amazon Bedrock is serverless, you don't have to manage any infrastructure, and you can securely integrate and deploy generative AI capabilities into your applications using the AWS services you are already familiar with.

Supercharging LLMs: Delivering Value with RAG and Bedrock Agents

Large language models (LLMs) have demonstrated remarkable abilities in natural language understanding and generation. However, LLMs are limited by the knowledge contained in their static training data. Retrieval-augmented generation (RAG) is a technique that enables LLMs to access and incorporate domain specific data or proprietary information securely from various knowledge sources, significantly extending their capabilities. RAG retrieves relevant passages from a knowledge base and provides them as additional context to the LLM, allowing it to generate outputs grounded in current, factual information.

Amazon Bedrock agents further enhance RAG by orchestrating multistep workflows involving foundation models, knowledge bases, and API calls. Agents can break down complex user queries, retrieve pertinent data via RAG, reason over the retrieved information, and generate comprehensive responses - all without manual engineering. The agent traces its reasoning steps, enabling developers to refine its behavior.

This session will explore how RAG and Bedrock agents can be leveraged to supercharge LLMs for knowledge-intensive applications. We will examine the key components of a RAG architecture, best practices for constructing knowledge bases, and methods for optimizing retrieval. Example applications in question answering, task completion, and open-ended dialogue will demonstrate the power of retrieval augmentation. Finally, we will discuss practical considerations and future directions for RAG and agent-based systems.

By harnessing external knowledge through RAG and Bedrock agents, we can push the boundaries of what LLMs can achieve, unlocking more capable, reliable, and scalable language technologies. Attendees will leave with an understanding of core RAG concepts and concrete ideas for implementing retrieval augmentation in their own project.