Category: Learning Levels

Gardenia Technologies, a data analytics company, partnered with the AWS Prototyping and Cloud Engineering (PACE) team to develop Report GenAI, a fully automated ESG reporting solution powered by the latest generative AI models on Amazon Bedrock. This post dives deep into the technology behind an agentic search solution using tooling with Retrieval Augmented Generation (RAG) and text-to-SQL capabilities to help customers reduce ESG reporting time by up to 75%. We demonstrate how AWS serverless technology, combined with agents in Amazon Bedrock, are used to build scalable and highly flexible agent-based document assistant applications.

In this post, we demonstrate how to use Amazon Bedrock and LangGraph to build a personalized customer support experience for an ecommerce retailer. By integrating the Mistral Large 2 and Pixtral Large models, we guide you through automating key customer support workflows such as ticket categorization, order details extraction, damage assessment, and generating contextual responses.

In this post, we demonstrate how Amazon Bedrock Guardrails helps block harmful and undesirable multimodal content. Using a healthcare insurance call center scenario, we walk through the process of configuring and testing various guardrails.

In this series of posts, you will learn how to build intelligent AI voice agents using Pipecat, an open-source framework for voice and multimodal conversational AI agents, with foundation models on Amazon Bedrock. It includes high-level reference architectures, best practices and code samples to guide your implementation.

In this post, we discuss how an enterprise with multiple accounts can access a shared Amazon SageMaker HyperPod cluster for running their heterogenous workloads. We use SageMaker HyperPod task governance to enable this feature.

Contextual retrieval enhances traditional RAG by adding chunk-specific explanatory context to each chunk before generating embeddings. This approach enriches the vector representation with relevant contextual information, enabling more accurate retrieval of semantically related content when responding to user queries. In this post, we demonstrate how to use contextual retrieval with Anthropic and Amazon Bedrock Knowledge Bases.

In this post, we share how Impel enhances the automotive dealership customer experience with fine-tuned LLMs on SageMaker.

We’ve witnessed remarkable advances in model capabilities as generative AI companies have invested in developing their offerings. Language models such as Anthropic’s Claude Opus 4 & Sonnet 4 and Amazon Nova on Amazon Bedrock can reason, write, and generate responses with increasing sophistication. But even as these models grow more powerful, they can only work […]

This blog post presents an MLOps solution that uses AWS Cloud Development Kit (AWS CDK) and services like AWS Step Functions, Amazon EventBridge and Amazon Personalize to automate provisioning resources for data preparation, model training, deployment, and monitoring for Amazon Personalize.

When a regulatory body like the US Food and Drug Administration (FDA) introduces changes to regulations, organizations are required to evaluate the changes against their internal SOPs. When necessary, they must update their SOPs to align with the regulation changes and maintain compliance. In this post, we show different approaches using Amazon Bedrock to identify relationships between regulation changes and SOPs.