AI

Improve RAG performance using Cohere Rerank

This post is co-written with Pradeep Prabhakaran from Cohere. Retrieval Augmented Generation (RAG) is a powerful technique that can help enterprises develop generative artificial intelligence (AI) apps that integrate real-time data and enable rich, interactive conversations using proprietary data. RAG allows these AI applications to tap into external, reliable sources of domain-specific knowledge, enriching the

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Unlock AWS Cost and Usage insights with generative AI powered by Amazon Bedrock

Managing cloud costs and understanding resource usage can be a daunting task, especially for organizations with complex AWS deployments. AWS Cost and Usage Reports (AWS CUR) provides valuable data insights, but interpreting and querying the raw data can be challenging. In this post, we explore a solution that uses generative artificial intelligence (AI) to generate

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Streamline workflow orchestration of a system of enterprise APIs using chaining with Amazon Bedrock Agents

Intricate workflows that require dynamic and complex API orchestration can often be complex to manage. In industries like insurance, where unpredictable scenarios are the norm, traditional automation falls short, leading to inefficiencies and missed opportunities. With the power of intelligent agents, you can simplify these challenges. In this post, we explore how chaining domain-specific agents

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Build ultra-low latency multimodal generative AI applications using sticky session routing in Amazon

Amazon SageMaker is a fully managed machine learning (ML) service. With SageMaker, data scientists and developers can quickly and confidently build, train, and deploy ML models into a production-ready hosted environment. SageMaker provides a broad selection of ML infrastructure and model deployment options to help meet your ML inference needs. It also helps scale your

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