Category: Generative AI

In this post, I will share some of these customers’ remarkable journeys, offering practical insights for any organization looking to harness the power of generative AI.

Today, we’re announcing a significant enhancement to Amazon Bedrock Guardrails: AWS Identity and Access Management (IAM) policy-based enforcement. This powerful capability enables security and compliance teams to establish mandatory guardrails for every model inference call, making sure organizational safety policies are consistently enforced across AI interactions. This feature enhances AI governance by enabling centralized control over guardrail implementation.

In this post, we explore using Amazon Bedrock to create a text-to-SQL application using RAG. We use Anthropic’s Claude 3.5 Sonnet model to generate SQL queries, Amazon Titan in Amazon Bedrock for text embedding and Amazon Bedrock to access these models.

As the healthcare industry continues to embrace digital transformation, solutions that combine advanced technologies like audio-to-text translation and LLMs will become increasingly valuable in addressing key challenges, such as patient education, engagement, and empowerment. In this post, we discuss possible use cases for combining speech recognition technology with LLMs, and how the solution can revolutionize clinical trials.

Today, we’re announcing computer use support within Amazon Bedrock Agents using Anthropic’s Claude 3.5 Sonnet V2 and Anthropic’s Claude Sonnet 3.7 models on Amazon Bedrock. This integration brings Anthropic’s visual perception capabilities as a managed tool within Amazon Bedrock Agents, providing you with a secure, traceable, and managed way to implement computer use automation in your workflows.

This post focuses on RAG evaluation with Amazon Bedrock Knowledge Bases, provides a guide to set up the feature, discusses nuances to consider as you evaluate your prompts and responses, and finally discusses best practices. By the end of this post, you will understand how the latest Amazon Bedrock evaluation features can streamline your approach to AI quality assurance, enabling more efficient and confident development of RAG applications.

This post provides an overview of a custom solution developed for GoDaddy, a domain registrar, registry, web hosting, and ecommerce company that seeks to make entrepreneurship more accessible by using generative AI to provide personalized business insights to over 21 million customers. In this collaboration, the Generative AI Innovation Center team created an accurate and cost-efficient generative AI–based solution using batch inference in Amazon Bedrock, helping GoDaddy improve their existing product categorization system.

The integration of generative AI agents into business processes is poised to accelerate as organizations recognize the untapped potential of these technologies. Advancements in multimodal artificial intelligence (AI), where agents can understand and generate not just text but also images, audio, and video, will further broaden their applications. This post will discuss agentic AI driven architecture and ways of implementing.

In this post, we dive deeper into one of MaestroQA’s key features—conversation analytics, which helps support teams uncover customer concerns, address points of friction, adapt support workflows, and identify areas for coaching through the use of Amazon Bedrock. We discuss the unique challenges MaestroQA overcame and how they use AWS to build new features, drive customer insights, and improve operational inefficiencies.

In this post, we demonstrate how to optimize hosting DeepSeek-R1 distilled models with Hugging Face Text Generation Inference (TGI) on Amazon SageMaker AI.