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What is Customer Science? Is this the next wave of change?

Beyond Philosophy

Customer Science to me in the integration between a number of existing disciplines; Behavioral Science, Technology (AI) and Big data. We often say there is a big difference between what Customers say and what they do. Use Customer Science to document how your efforts work or don’t. Key Takeaways.

Finance 396
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Revolutionizing clinical trials with the power of voice and AI

AWS Machine Learning

Site monitors conduct on-site visits, interview personnel, and verify documentation to assess adherence to protocols and regulatory requirements. However, this process can be time-consuming and prone to errors, particularly when dealing with extensive audio recordings and voluminous documentation.

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Reducing hallucinations in LLM agents with a verified semantic cache using Amazon Bedrock Knowledge Bases

AWS Machine Learning

The agent knowledge base stores Amazon Bedrock service documentation, while the cache knowledge base contains curated and verified question-answer pairs. For this example, you will ingest Amazon Bedrock documentation in the form of the User Guide PDF into the Amazon Bedrock knowledge base. This will be the primary dataset.

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Intelligent healthcare forms analysis with Amazon Bedrock

AWS Machine Learning

The healthcare industry generates and collects a significant amount of unstructured textual data, including clinical documentation such as patient information, medical history, and test results, as well as non-clinical documentation like administrative records. read()) answer = response_body.get("content")[0].get("text")

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Executive Report: The Customer Data Too Often Overlooked by the C-Suite

A recent Calabrio research study of more than 1,000 C-Suite executives has revealed leaders are missing a key data stream – voice of the customer data. Download the report to learn how executives can find and use VoC data to make more informed business decisions.

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Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

AWS Machine Learning

Knowledge Bases for Amazon Bedrock automates synchronization of your data with your vector store, including diffing the data when it’s updated, document loading, and chunking, as well as semantic embedding. RAG is a popular technique that combines the use of private data with large language models (LLMs). txt) Markdown (.md)

APIs 131
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Build a custom UI for Amazon Q Business

AWS Machine Learning

Amazon Q returns the response as a JSON object (detailed in the Amazon Q documentation ). sourceAttributions – The source documents used to generate the conversation response. In Retrieval Augmentation Generation (RAG), this always refers to one or more documents from enterprise knowledge bases that are indexed in Amazon Q.

APIs 128