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Evaluate Amazon Bedrock Agents with Ragas and LLM-as-a-judge

AI agents are quickly becoming an integral part of customer workflows across industries by automating complex tasks, enhancing decision-making, and streamlining operations. However, the adoption of AI agents in production systems requires scalable evaluation pipelines. Robust agent evaluation enables...

Enterprise-grade natural language to SQL generation using LLMs: Balancing accuracy, latency, and scale

This blog post is co-written with Renuka Kumar and Thomas Matthew from Cisco. Enterprise data by its very nature spans diverse data domains, such as security, finance, product, and HR. Data across these domains is often maintained across disparate data...

Combine keyword and semantic search for text and images using Amazon Bedrock and Amazon OpenSearch Service

Customers today expect to find products quickly and efficiently through intuitive search functionality. A seamless search journey not only enhances the overall user experience, but also directly impacts key business metrics such as conversion rates, average order value, and...

AWS Field Experience reduced cost and delivered low latency and high performance with Amazon Nova Lite foundation model

AWS Field Experience (AFX) empowers Amazon Web Services (AWS) sales teams with generative AI solutions built on Amazon Bedrock, improving how AWS sellers and customers interact. The AFX team uses AI to automate tasks and provide intelligent insights and...

Protect sensitive data in RAG applications with Amazon Bedrock

Retrieval Augmented Generation (RAG) applications have become increasingly popular due to their ability to enhance generative AI tasks with contextually relevant information. Implementing RAG-based applications requires careful attention to security, particularly when handling sensitive data. The protection of personally...

Build an AI-powered document processing platform with open source NER model and LLM on Amazon SageMaker

Archival data in research institutions and national laboratories represents a vast repository of historical knowledge, yet much of it remains inaccessible due to factors like limited metadata and inconsistent labeling. Traditional keyword-based search mechanisms are often insufficient for locating...
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