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Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

AWS Machine Learning - AI

Knowledge Bases for Amazon Bedrock allows you to build performant and customized Retrieval Augmented Generation (RAG) applications on top of AWS and third-party vector stores using both AWS and third-party models. RAG is a popular technique that combines the use of private data with large language models (LLMs).

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SAP and Nvidia expand partnership to aid customers with gen AI

CIO

At the end of the day, customers want the best experience, the best performance, or the lowest price in order to consume LLMs within their workflows.” Finally, the new Nvidia NIM inference microservices, also announced today, will help customers optimize inference performance across their SAP infrastructure.

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

AWS Machine Learning - AI

At the forefront of this evolution sits Amazon Bedrock , a fully managed service that makes high-performing foundation models (FMs) from Amazon and other leading AI companies available through an API. The following demo recording highlights Agents and Knowledge Bases for Amazon Bedrock functionality and technical implementation details.

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How CIOs use AI to elevate CX services

CIO

The company’s customers can use an interface called expereoOne to analyze global network performance, and Elms is keen to bolster CX efforts further with AI. The firm is exploring Salesforce’s ServiceGPT and Einstein technologies, and they’re building a knowledge base on the provider’s Sales Cloud platform as well.

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Vector Database vs. Knowledge Graph: Making the Right Choice When Implementing RAG

CIO

Choosing the right data architecture Currently, there are two primary technologies that are used to organize the data and the context needed for a RAG framework to generate accurate, relevant responses: Vector Databases (DBs) and Knowledge Graphs. Vector DBs perform so well in these contexts because they can perform semantic searches.

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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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AI bots for customer experience: trends, insights, and examples

CIO

The hype surrounding AI-based voice and chatbots is evident, but do they deliver? Most still perform only extremely basic tasks and often mirror the poor practices of traditional IVRs. Customers may be open to the idea, but only 30% believe that chatbots and virtual assistants make it easier to address their service issues.

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