Category: Generative AI

In this post, we show you how DoorDash built a generative AI agent using Amazon Connect, Amazon Lex, and Amazon Bedrock Knowledge Bases to provide a low-latency, self-service experience for their delivery workers.

In this post, we explain how to use Anthropic Claude on Amazon Bedrock to generate synthetic data for evaluating your RAG system.

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 blog post, we explore how you can use Amazon Q Business to build generative AI assistants that enhance employee experience and boost productivity. Amazon Q Business seamlessly integrates with internal data sources, knowledge bases, and productivity tools to equip your workforce with instant access to information, automated tasks, and personalized support.

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 present an automated solution to provide a consistent and responsible personalization experience for your customers by using smaller LLMs for website personalization tailored to businesses and industries. This decomposes the complex task into subtasks handled by task / domain adopted LLMs, adhering to company guidelines and human expertise.

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 show you how to use Cohere Rerank to improve search efficiency and accuracy in Retrieval Augmented Generation (RAG) systems.

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.