Artificial Intelligence
Category: Technical How-to
Build powerful RAG pipelines with LlamaIndex and Amazon Bedrock
In this post, we show you how to use LlamaIndex with Amazon Bedrock to build robust and sophisticated RAG pipelines that unlock the full potential of LLMs for knowledge-intensive tasks.
Evaluating prompts at scale with Prompt Management and Prompt Flows for Amazon Bedrock
In this post, we demonstrate how to implement an automated prompt evaluation system using Amazon Bedrock so you can streamline your prompt development process and improve the overall quality of your AI-generated content.
Deploy Amazon SageMaker pipelines using AWS Controllers for Kubernetes
In this post, we show how ML engineers familiar with Jupyter notebooks and SageMaker environments can efficiently work with DevOps engineers familiar with Kubernetes and related tools to design and maintain an ML pipeline with the right infrastructure for their organization. This enables DevOps engineers to manage all the steps of the ML lifecycle with the same set of tools and environment they are used to.
Effectively manage foundation models for generative AI applications with Amazon SageMaker Model Registry
In this post, we explore the new features of Model Registry that streamline foundation model (FM) management: you can now register unzipped model artifacts and pass an End User License Agreement (EULA) acceptance flag without needing users to intervene.
Build an ecommerce product recommendation chatbot with Amazon Bedrock Agents
In this post, we show you how to build an ecommerce product recommendation chatbot using Amazon Bedrock Agents and foundation models (FMs) available in Amazon Bedrock.
Build a generative AI image description application with Anthropic’s Claude 3.5 Sonnet on Amazon Bedrock and AWS CDK
In this post, we delve into the process of building and deploying a sample application capable of generating multilingual descriptions for multiple images with a Streamlit UI, AWS Lambda powered with the Amazon Bedrock SDK, and AWS AppSync driven by the open source Generative AI CDK Constructs.
Implementing tenant isolation using Agents for Amazon Bedrock in a multi-tenant environment
In this blog post, we will show you how to implement tenant isolation using Amazon Bedrock agents within a multi-tenant environment. We’ll demonstrate this using a sample multi-tenant e-commerce application that provides a service for various tenants to create online stores. This application will use Amazon Bedrock agents to develop an AI assistant or chatbot capable of providing tenant-specific information, such as return policies and user-specific information like order counts and status updates.
Connect the Amazon Q Business generative AI coding companion to your GitHub repositories with Amazon Q GitHub (Cloud) connector
In this post, we show you how to perform natural language queries over the indexed GitHub (Cloud) data using the AI-powered chat interface provided by Amazon Q Business. We also cover how Amazon Q Business applies access control lists (ACLs) associated with the indexed documents to provide permissions-filtered responses.
Elevate customer experience through an intelligent email automation solution using Amazon Bedrock
In this post, we show you how to use Amazon Bedrock to automate email responses to customer queries. With our solution, you can identify the intent of customer emails and send an automated response if the intent matches your existing knowledge base or data sources. If the intent doesn’t have a match, the email goes to the support team for a manual response.
Index website contents using the Amazon Q Web Crawler connector for Amazon Q Business
In this post, we demonstrate how to create an Amazon Q Business application and index website contents using the Amazon Q Web Crawler connector for Amazon Q Business. We use two data sources (websites) here. The first data source is an employee onboarding guide from a fictitious company, which requires basic authentication. We demonstrate how to set up authentication for the Web Crawler. The second data source is the official documentation for Amazon Q Business. For this data source, we demonstrate how to apply advanced settings to instruct the Web Crawler to crawl only pages and links related to Amazon Q Business.