Category: Technical How-to

In this post, we present a real-world use case analyzing the Diabetes 130-US hospitals dataset to develop an ML model that predicts the likelihood of readmission after discharge.

In this post, we demonstrate how to use Amazon Bedrock Agents with a web search API to integrate dynamic web content in your generative AI application.

In this post, we show you how the new document-level report in Amazon Kendra provides enhanced visibility and observability into the document processing lifecycle during data source sync jobs.

In this post, we walk through how AWS can help accelerate a brand’s creative efforts with access to a powerful image-to-image model from Stable Diffusion available on Amazon Bedrock to interactively create and edit art and logo images.

In this post, we demonstrate a solution using Amazon FSx for NetApp ONTAP with Amazon Bedrock to provide a RAG experience for your generative AI applications on AWS by bringing company-specific, unstructured user file data to Amazon Bedrock in a straightforward, fast, and secure way.

In this post, we explore a solution that uses generative artificial intelligence (AI) to generate a SQL query from a user’s question in natural language. This solution can simplify the process of querying CUR data stored in an Amazon Athena database using SQL query generation, running the query on Athena, and representing it on a web portal for ease of understanding.

In this post, we explore how chaining domain-specific agents using Amazon Bedrock Agents can transform a system of complex API interactions into streamlined, adaptive workflows, empowering your business to operate with agility and precision.

In this post, we provide a step-by-step guide for creating an enterprise ready RAG application such as a question answering bot. We use the Llama3-8B FM for text generation and the BGE Large EN v1.5 text embedding model for generating embeddings from Amazon SageMaker JumpStart.

In this post, we discuss how generative artificial intelligence (AI) can help health insurance plan members get the information they need. The solution presented in this post not only enhances the member experience by providing a more intuitive and user-friendly interface, but also has the potential to reduce call volumes and operational costs for healthcare payers and plans.

In this post, we show you how to enhance the performance of Meta Llama 3 8B Instruct by fine-tuning it using direct preference optimization (DPO) on data collected with SageMaker Ground Truth.