Category: Amazon Bedrock

In this post we describe how AWS generative AI solutions (including Amazon Bedrock, Amazon Q Developer, and Amazon Q Business) can further enhance TechOps productivity, reduce time to resolve issues, enhance customer experience, standardize operating procedures, and augment knowledge bases.

In this post, we take a closer look at Amazon Bedrock Agents. They empower you to build intelligent and context-aware generative AI applications, streamlining complex workflows and delivering natural, conversational user experiences.

In this post, we share how Domo, a cloud-centered data experiences innovator is using Amazon Bedrock to provide a flexible and powerful AI solution.

In this post, we illustrate how Vidmob, a creative data company, worked with the AWS Generative AI Innovation Center (GenAIIC) team to uncover meaningful insights at scale within creative data using 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.

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.

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.

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.

In this post, we provide insights and practical examples to help balance and optimize the prompt engineering workflow. We focus on advanced prompt techniques and best practices for the models provided in Amazon Bedrock, a fully managed service that offers a choice of high-performing foundation models from leading AI companies such as Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API. With these prompting techniques, developers and researchers can harness the full capabilities of Amazon Bedrock, providing clear and concise communication while mitigating potential risks or undesirable outputs.

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.