Category: HAQM Bedrock

In this post, we demonstrate how to implement this evaluation framework using HAQM Bedrock, compare the performance of different generator models, including Anthropic’s Claude and HAQM Nova on HAQM Bedrock, and showcase how to use the new RAG evaluation feature to optimize knowledge base parameters and assess retrieval quality.

Pattern is a leader in ecommerce acceleration, helping brands navigate the complexities of selling on marketplaces and achieve profitable growth through a combination of proprietary technology and on-demand expertise. In this post, we share how Pattern uses AWS services to process trillions of data points to deliver actionable insights, optimizing product listings across multiple services.

In this guide, we walk you through step-by-step instructions for configuring cross-account access for HAQM Bedrock Custom Model Import, covering both non-encrypted and AWS Key Management Service (AWS KMS) based encrypted scenarios.

In 2021, Applus+ IDIADA, a global partner to the automotive industry with over 30 years of experience supporting customers in product development activities through design, engineering, testing, and homologation services, established the Digital Solutions department. In this post, we showcase the research process undertaken to develop a classifier for human interactions in this AI-based environment using HAQM Bedrock.

This post demonstrates how HAQM Bedrock Agents, combined with action groups and generative AI models, streamlines and accelerates the resolution of Terraform errors while maintaining compliance with environment security and operational guidelines.

This intelligent document processing solution uses HAQM Bedrock FMs to orchestrate a sophisticated workflow for handling multi-page healthcare documents with mixed content types. The solution uses the FM’s tool use capabilities, accessed through the HAQM Bedrock Converse API. This enables the FMs to not just process text, but to actively engage with various external tools and APIs to perform complex document analysis tasks.

This post introduces a solution to reduce hallucinations in Large Language Models (LLMs) by implementing a verified semantic cache using HAQM Bedrock Knowledge Bases, which checks if user questions match curated and verified responses before generating new answers. The solution combines the flexibility of LLMs with reliable, verified answers to improve response accuracy, reduce latency, and lower costs while preventing potential misinformation in critical domains such as healthcare, finance, and legal services.

In this post, we explore how you can use LLMs with advanced Retrieval Augmented Generation (RAG) to generate high-quality synthetic data for a finance domain use case. You can use the same technique for synthetic data for other business domain use cases as well. For this post, we demonstrate how to generate counterparty risk (CR) data, which would be beneficial for over-the-counter (OTC) derivatives that are traded directly between two parties, without going through a formal exchange.

Verisk’s Premium Audit Advisory Service is the leading source of technical information and training for premium auditors and underwriters. In this post, we describe the development of the customer support process in PAAS, incorporating generative AI, the data, the architecture, and the evaluation of the results. Conversational AI assistants are rapidly transforming customer and employee support.

In this post, we explore how Automated Reasoning checks work through various common FSI scenarios such as insurance legal triaging, underwriting rules validation, and claims processing.