Category: Amazon Bedrock

Today, we are excited to announce general availability of batch inference for Amazon Bedrock. This new feature enables organizations to process large volumes of data when interacting with foundation models (FMs), addressing a critical need in various industries, including call center operations. In this post, we demonstrate the capabilities of batch inference using call center transcript summarization as an example.

This post explores an innovative application of large language models (LLMs) to automate the process of customer review analysis. LLMs are a type of foundation model (FM) that have been pre-trained on vast amounts of text data. This post discusses how LLMs can be accessed through Amazon Bedrock to build a generative AI solution that automatically summarizes key information, recognizes the customer sentiment, and generates actionable insights from customer reviews. This method shows significant promise in saving human analysts time while producing high-quality results. We examine the approach in detail, provide examples, highlight key benefits and limitations, and discuss future opportunities for more advanced product review summarization through generative AI.

Today, physicians spend about 49% of their workday documenting clinical visits, which impacts physician productivity and patient care. Did you know that for every eight hours that office-based physicians have scheduled with patients, they spend more than five hours in the EHR? As a consequence, healthcare practitioners exhibit a pronounced inclination towards conversational intelligence solutions, […]

This post explores how Accenture used the customization capabilities of Knowledge Bases for Amazon Bedrock to incorporate their data processing workflow and custom logic to create a custom chunking mechanism that enhances the performance of Retrieval Augmented Generation (RAG) and unlock the potential of your PDF data.

QnABot on AWS (an AWS Solution) now provides access to Amazon Bedrock foundational models (FMs) and Knowledge Bases for Amazon Bedrock, a fully managed end-to-end Retrieval Augmented Generation (RAG) workflow. You can now provide contextual information from your private data sources that can be used to create rich, contextual, conversational experiences. In this post, we discuss how to use QnABot on AWS to deploy a fully functional chatbot integrated with other AWS services, and delight your customers with human agent like conversational experiences.

In this post, we explore using the Anthropic Claude 3 on Amazon Bedrock large language model (LLM). Amazon Bedrock provides access to several LLMs, such as Anthropic Claude 3, which can be used to generate semi-structured data relevant to the healthcare industry. This can be particularly useful for creating various healthcare-related forms, such as patient intake forms, insurance claim forms, or medical history questionnaires.

This post provides an overview of a custom solution developed by the AWS Generative AI Innovation Center (GenAIIC) for Deltek, a globally recognized standard for project-based businesses in both government contracting and professional services. Deltek serves over 30,000 clients with industry-specific software and information solutions. 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. The solution uses AWS services including Amazon Textract, Amazon OpenSearch Service, and Amazon Bedrock.

As one of the largest AWS customers, Twilio engages with data, artificial intelligence (AI), and machine learning (ML) services to run their daily workloads. This post highlights how Twilio enabled natural language-driven data exploration of business intelligence (BI) data with RAG and Amazon Bedrock.

AI chatbots and virtual assistants have become increasingly popular in recent years thanks the breakthroughs of large language models (LLMs). Trained on a large volume of datasets, these models incorporate memory components in their architectural design, allowing them to understand and comprehend textual context. Most common use cases for chatbot assistants focus on a few […]

With my blog post from June, I started a series that highlights the key factors that are driving customers to choose Amazon Bedrock. I explored how Bedrock enables customers to build a secure, compliant foundation for generative AI applications. Now I’d like to turn to a slightly more technical, but equally important differentiator for Bedrock—the multiple techniques that you can use to customize models and meet your specific business needs.