Category: HAQM Bedrock

In this post, we explain how F1 and AWS have developed a root cause analysis (RCA) assistant powered by HAQM Bedrock to reduce manual intervention and accelerate the resolution of recurrent operational issues during races from weeks to minutes. The RCA assistant enables the F1 team to spend more time on innovation and improving its services, ultimately delivering an exceptional experience for fans and partners. The successful collaboration between F1 and AWS showcases the transformative potential of generative AI in empowering teams to accomplish more in less time.

In this post, we explore how to build an application using HAQM Bedrock inline agents, demonstrating how a single AI assistant can adapt its capabilities dynamically based on user roles.

In this post, we explore what language embeddings are and how they can be used to enhance your application. We show how, by using the properties of embeddings, we can implement a real-time zero-shot classifier and can add powerful features such as semantic search.

In this post, we explore how to use HAQM Bedrock to generate synthetic training data to fine-tune an LLM. Additionally, we provide concrete evaluation results that showcase the power of synthetic data in fine-tuning when data is scarce.

This blog post explores LLM-as-a-judge on HAQM Bedrock Model Evaluation, providing comprehensive guidance on feature setup, evaluating job initiation through both the console and Python SDK and APIs, and demonstrating how this innovative evaluation feature can enhance generative AI applications across multiple metric categories including quality, user experience, instruction following, and safety.

In this post, we explore the movement of RAG applications from their proof of concept or minimal viable product (MVP) phase to full-fledged production systems. When transitioning a RAG application from a proof of concept to a production-ready system, optimization becomes crucial to make sure the solution is reliable, cost-effective, and high-performing.

In this post, we present a streamlined approach to deploying an AI-powered agent by combining HAQM Bedrock Agents and a foundation model (FM). We guide you through the process of configuring the agent and implementing the specific logic required for the virtual meteorologist to provide accurate weather-related responses.

AWS and Appian’s collaboration marks a significant advancement in business process automation. By using the power of HAQM Bedrock and Anthropic’s Claude models, Appian empowers enterprises to optimize and automate processes for greater efficiency and effectiveness. This blog post will cover how Appian AI skills build automation into organizations’ mission-critical processes to improve operational excellence, reduce costs, and build scalable solutions.

Untold Studios is a tech-driven, leading creative studio specializing in high-end visual effects and animation. This post details how we used HAQM Bedrock to create an AI assistant (Untold Assistant), providing artists with a straightforward way to access our internal resources through a natural language interface integrated directly into their existing Slack workflow.

This blog post provides a comprehensive guide to implementing robust safety protections for DeepSeek-R1 and other open weight models using HAQM Bedrock Guardrails. By following this guide, you’ll learn how to use the advanced capabilities of DeepSeek models while maintaining strong security controls and promoting ethical AI practices.