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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. If you want more control, Knowledge Bases lets you control the chunking strategy through a set of preconfigured options.

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Tractor Supply enlists AI to deliver ‘legendary’ customer service

CIO

With information about products and availability constantly changing, Tractor Supply sees Hey GURA as a “knowledge base and a training platform,” says Rob Mills, chief technology, digital commerce, and strategy officer at Tractor Supply. One of our differentiators is we hire team members from the community,” he says.

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ChatGPT, the rise of generative AI

CIO

GPT stands for generative pre-trained transformer. Five years later, transformer architecture has evolved to create powerful models such as ChatGPT. ChatGPT was trained on a much larger dataset than its predecessors, with far more parameters. ChatGPT is a product of OpenAI. It’s only one example of generative AI.

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SAP and Nvidia expand partnership to aid customers with gen AI

CIO

RAG optimizes LLMs by giving them the ability to reference authoritative knowledge bases outside their training data. “There are tons of documents that are not residing in an SAP system,” Herzig said. Artificial Intelligence, Data Architecture, Data Science, Digital Transformation, Generative AI, IT Leadership, Nvidia, SAP

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Implementing Knowledge Bases for Amazon Bedrock in support of GDPR (right to be forgotten) requests

AWS Machine Learning - AI

FMs are trained on vast quantities of data, allowing them to be used to answer questions on a variety of subjects. Knowledge Bases for Amazon Bedrock is a fully managed RAG capability that allows you to customize FM responses with contextual and relevant company data. The following diagram depicts a high-level RAG architecture.

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Vitech uses Amazon Bedrock to revolutionize information access with AI-powered chatbot

AWS Machine Learning - AI

In this blog, we walkthrough the architectural components, evaluation criteria for the components selected by Vitech and the process flow of user interaction within VitechIQ. Fine-tuning would allow Vitech to train the model on a small sample set, thereby allowing the model to provide response using Vitech’s vocabulary.

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Simplify data prep for generative AI with Amazon SageMaker Data Wrangler

AWS Machine Learning - AI

However, these models require massive amounts of clean, structured training data to reach their full potential. LLM has not seen the documentation during the training or fine-tuning stage, thus wouldn’t be able to answer the question and most probably will hallucinate. Access to Amazon OpenSearch as a vector database.