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How Deltek uses Amazon Bedrock for question and answering on government solicitation documents

AWS Machine Learning

Question and answering (Q&A) using documents is a commonly used application in various use cases like customer support chatbots, legal research assistants, and healthcare advisors. In this collaboration, the AWS GenAIIC team created a RAG-based solution for Deltek to enable Q&A on single and multiple government solicitation documents.

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

AWS Machine Learning

Such data often lacks the specialized knowledge contained in internal documents available in modern businesses, which is typically needed to get accurate answers in domains such as pharmaceutical research, financial investigation, and customer support. This task involves answering analytical reasoning questions.

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Dynamic Scripting: Crafting Personalized Conversations with Call Center Software

Hodusoft

Dynamic Scripting: Crafting Personalized Conversations with Call Center Software In the contemporary business world, focusing on customers’ requirements and delivering a personalized experience is essential. Rather than sticking to a fixed script, it can change on the spot depending on what the customer is saying or doing.

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Few-shot prompt engineering and fine-tuning for LLMs in Amazon Bedrock

AWS Machine Learning

Traditionally, earnings call scripts have followed similar templates, making it a repeatable task to generate them from scratch each time. On the other hand, generative artificial intelligence (AI) models can learn these templates and produce coherent scripts when fed with quarterly financial data.

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Find answers accurately and quickly using Amazon Q Business with the SharePoint Online connector

AWS Machine Learning

Accelerate research and analysis – Instead of manually searching through SharePoint documents, users can use Amazon Q to quickly find relevant information, summaries, and insights to support their research and decision-making. The site content space also provides access to add lists, pages, document libraries, and more.

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Create a multimodal chatbot tailored to your unique dataset with Amazon Bedrock FMs

AWS Machine Learning

For many of these use cases, businesses are building Retrieval Augmented Generation (RAG) style chat-based assistants, where a powerful LLM can reference company-specific documents to answer questions relevant to a particular business or use case. This may be useful for later chat assistant analytics.

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Get started quickly with AWS Trainium and AWS Inferentia using AWS Neuron DLAMI and AWS Neuron DLC

AWS Machine Learning

When a Neuron SDK is released, you’ll now be notified of the support for Neuron DLAMIs and Neuron DLCs in the Neuron SDK release notes, with a link to the AWS documentation containing the DLAMI and DLC release notes. You also need the ML job scripts ready with a command to invoke them. Starting with the AWS Neuron 2.18

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