Category: Learning Levels

In this post, we show how to create an application using Amazon Q Business with Jira integration that used a dataset containing a Trusted Advisor detailed report. This solution demonstrates how to use new generative AI services like Amazon Q Business to get data insights faster and make them actionable.

In this post, we show how you can automate language localization through translating documents using Amazon Web Services (AWS). The solution combines Amazon Bedrock and AWS Serverless technologies, a suite of fully managed event-driven services for running code, managing data, and integrating applications—all without managing servers.

In this post, we explore how AWS services can be seamlessly integrated with open source tools to help establish a robust red teaming mechanism within your organization. Specifically, we discuss Data Reply’s red teaming solution, a comprehensive blueprint to enhance AI safety and responsible AI practices.

This post demonstrates how AWS LLM League’s gamified enablement accelerates partners’ practical AI development capabilities, while showcasing how fine-tuning smaller language models can deliver cost-effective, specialized solutions for specific industry needs.

In this post, we introduced the Open Source Bedrock Agent Evaluation framework, a Langfuse-integrated solution that streamlines the agent development process. We demonstrated how this evaluation framework can be integrated with pharmaceutical research agents. We used it to evaluate agent performance against biomarker questions and sent traces to Langfuse to view evaluation metrics across question types.

In this post, we walk you through how to build a hybrid search solution using OpenSearch Service powered by multimodal embeddings from the Amazon Titan Multimodal Embeddings G1 model through Amazon Bedrock. This solution demonstrates how you can enable users to submit both text and images as queries to retrieve relevant results from a sample retail image dataset.

In the first post of this series, we introduced a comprehensive evaluation framework for Amazon Q Business, a fully managed Retrieval Augmented Generation (RAG) solution that uses your company’s proprietary data without the complexity of managing large language models (LLMs). The first post focused on selecting appropriate use cases, preparing data, and implementing metrics to […]

Today, we’re happy to announce the general availability of Amazon Bedrock Intelligent Prompt Routing. In this blog post, we detail various highlights from our internal testing, how you can get started, and point out some caveats and best practices. We encourage you to incorporate Amazon Bedrock Intelligent Prompt Routing into your new and existing generative AI applications.

Today, we are excited to announce the availability of Prompt Optimization on Amazon Bedrock. With this capability, you can now optimize your prompts for several use cases with a single API call or a click of a button on the Amazon Bedrock console. In this blog post, we discuss how Prompt Optimization improves the performance of large language models (LLMs) for intelligent text processing task in Yuewen Group.

In this post, we explore the importance of evaluating LLMs in the context of generative AI applications, highlighting the challenges posed by issues like hallucinations and biases. We introduced a comprehensive solution using AWS services to automate the evaluation process, allowing for continuous monitoring and assessment of LLM performance. By using tools like the FMeval Library, Ragas, LLMeter, and Step Functions, the solution provides flexibility and scalability, meeting the evolving needs of LLM consumers.