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ChatGPT, the rise of generative AI

CIO

A transformer is a type of AI deep learning model that was first introduced by Google in a research paper in 2017. Five years later, transformer architecture has evolved to create powerful models such as ChatGPT. ChatGPT’s conversational interface is a distinguished method of accessing its knowledge.

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Build a contextual chatbot application using Knowledge Bases for Amazon Bedrock

AWS Machine Learning - AI

One way to enable more contextual conversations is by linking the chatbot to internal knowledge bases and information systems. Integrating proprietary enterprise data from internal knowledge bases enables chatbots to contextualize their responses to each user’s individual needs and interests.

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Automate the insurance claim lifecycle using Agents and Knowledge Bases for Amazon Bedrock

AWS Machine Learning - AI

You can now use Agents for Amazon Bedrock and Knowledge Bases for Amazon Bedrock to configure specialized agents that seamlessly run actions based on natural language input and your organization’s data. The following diagram illustrates the solution architecture. The following are some example prompts: Create a new claim.

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

AWS Machine Learning - AI

RAG allows models to tap into vast knowledge bases and deliver human-like dialogue for applications like chatbots and enterprise search assistants. Solution overview In this post, we demonstrate how to create a RAG-based application using LlamaIndex and an LLM. Download press releases to use as our external knowledge base.

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Generate customized, compliant application IaC scripts for AWS Landing Zone using Amazon Bedrock

AWS Machine Learning - AI

Traditionally, cloud engineers learning IaC would manually sift through documentation and best practices to write compliant IaC scripts. With Amazon Bedrock, teams can input high-level architectural descriptions and use generative AI to generate a baseline configuration of Terraform scripts.

AWS 97
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Enhance conversational AI with advanced routing techniques with Amazon Bedrock

AWS Machine Learning - AI

It uses the provided conversation history, action groups, and knowledge bases to understand the context and determine the necessary tasks. This is based on the instructions that are interpreted by the assistant as per the system prompt and user’s input. Additionally, you can access device historical data or device metrics.

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Boosting RAG-based intelligent document assistants using entity extraction, SQL querying, and agents with Amazon Bedrock

AWS Machine Learning - AI

To create AI assistants that are capable of having discussions grounded in specialized enterprise knowledge, we need to connect these powerful but generic LLMs to internal knowledge bases of documents. Then we introduce you to a more versatile architecture that overcomes these limitations.