Category: Generative AI

This post walks you through how to create a basic hallucination detection system for RAG-based applications. We also weigh the pros and cons of different methods in terms of accuracy, precision, recall, and cost.

Building on this foundation of specialized information extraction solutions and using the capabilities of SageMaker HyperPod, we collaborate with APOIDEA Group to explore the use of large vision language models (LVLMs) to further improve table structure recognition performance on banking and financial documents. In this post, we present our work and step-by-step code on fine-tuning the Qwen2-VL-7B-Instruct model using LLaMA-Factory on SageMaker HyperPod.

In this post, we share how Qualtrics built an AI platform powered by Amazon SageMaker and Amazon Bedrock.

This post is the first in a series where we will run multiple diffusion transformers on Trainium and Inferentia-powered instances. In this post, we show how you can deploy PixArt-Sigma to Trainium and Inferentia-powered instances.

In this post, we demonstrate a solution using Amazon Elastic Kubernetes Service (EKS) with Amazon Bedrock to build scalable and containerized RAG solutions for your generative AI applications on AWS while bringing your unstructured user file data to Amazon Bedrock in a straightforward, fast, and secure way.

Recognizing the transformative benefits of generative AI for enterprises, we at Hexagon’s Asset Lifecycle Intelligence division sought to enhance how users interact with our Enterprise Asset Management (EAM) products. Understanding these advantages, we partnered with AWS to embark on a journey to develop HxGN Alix, an AI-powered digital worker using AWS generative AI services. This blog post explores the strategy, development, and implementation of HxGN Alix, demonstrating how a tailored AI solution can drive efficiency and enhance user satisfaction.

Now with Amazon Bedrock, you can develop custom evaluation metrics for both model and RAG evaluations. This capability extends the LLM-as-a-judge framework that drives Amazon Bedrock Evaluations. In this post, we demonstrate how to use custom metrics in Amazon Bedrock Evaluations to measure and improve the performance of your generative AI applications according to your specific business requirements and evaluation criteria.

This post explores a financial assistant system that specializes in three key tasks: portfolio creation, company research, and communication. This post aims to illustrate the use of multiple specialized agents within the Amazon Bedrock multi-agent collaboration capability, with particular emphasis on their application in financial analysis.

In this post, we showcase how Dr. Kori Ramajoo, Dr. Sonia Brownsett, Prof. David Copland, from QARC, and Scott Harding, a person living with aphasia, used AWS services to develop WordFinder, a mobile, cloud-based solution that helps individuals with aphasia increase their independence through the use of AWS generative AI technology.

In this post, we share comprehensive best practices and scientific insights for fine-tuning Meta Llama 3.2 multimodal models on Amazon Bedrock. By following these guidelines, you can fine-tune smaller, more cost-effective models to achieve performance that rivals or even surpasses much larger models—potentially reducing both inference costs and latency, while maintaining high accuracy for your specific use case.