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ChatGPT, the rise of generative AI

CIO

Over the last few months, both business and technology worlds alike have been abuzz about ChatGPT, and more than a few leaders are wondering what this AI advancement means for their organizations. It’s only one example of generative AI. GPT stands for generative pre-trained transformer. What is ChatGPT?

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Delight your customers with great conversational experiences via QnABot, a generative AI chatbot

AWS Machine Learning - AI

QnABot on AWS (an AWS Solution) now provides access to Amazon Bedrock foundational models (FMs) and Knowledge Bases for Amazon Bedrock , a fully managed end-to-end Retrieval Augmented Generation (RAG) workflow. In turn, customers can ask a variety of questions and receive accurate answers powered by generative AI.

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Streamline generative AI development in Amazon Bedrock with Prompt Management and Prompt Flows (preview)

AWS Machine Learning - AI

These features are designed to accelerate the development, testing, and deployment of generative artificial intelligence (AI) applications, enabling developers and business users to create more efficient and effective solutions that are easier to maintain. The following diagram illustrates this workflow.

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Build a RAG-based QnA application using Llama3 models from SageMaker JumpStart

AWS Machine Learning - AI

Organizations generate vast amounts of data that is proprietary to them, and it’s critical to get insights out of the data for better business outcomes. Generative AI and foundation models (FMs) play an important role in creating applications using an organization’s data that improve customer experiences and employee productivity.

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Harnessing the Power of AWS Bedrock through CloudFormation

Perficient

The rapid advancement of artificial intelligence (AI) has led to the development of foundational models that form the bedrock of numerous AI applications. AWS Bedrock is Amazon Web Services’ comprehensive solution that leverages these models to provide robust AI and machine learning (ML) capabilities.

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Build knowledge-powered conversational applications using LlamaIndex and Llama 2-Chat

AWS Machine Learning - AI

Unlocking accurate and insightful answers from vast amounts of text is an exciting capability enabled by large language models (LLMs). When building LLM applications, it is often necessary to connect and query external data sources to provide relevant context to the model.

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Knowledge Bases for Amazon Bedrock now supports advanced parsing, chunking, and query reformulation giving greater control of accuracy in RAG based applications

AWS Machine Learning - AI

Knowledge Bases for Amazon Bedrock is a fully managed service that helps you implement the entire Retrieval Augmented Generation (RAG) workflow from ingestion to retrieval and prompt augmentation without having to build custom integrations to data sources and manage data flows, pushing the boundaries for what you can do in your RAG workflows.