Thought Leadership

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Tailor responsible AI with new safeguard tiers in Amazon Bedrock Guardrails

Amazon Bedrock, Amazon Bedrock Guardrails, Artificial Intelligence, Best Practices, Generative AI, Responsible AI, Technical How-to, Thought Leadership /

Amazon Bedrock Guardrails provides configurable safeguards to help build trusted generative AI applications at scale. It provides organizations with integrated safety and privacy safeguards that work across multiple foundation models (FMs), including models available in Amazon Bedrock, as well as models hosted outside Amazon Bedrock from other model providers and cloud providers. With the standalone […]

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Architect a mature generative AI foundation on AWS

Best Practices, Generative AI, Intermediate (200), Thought Leadership /

Generative AI applications seem simple—invoke a foundation model (FM) with the right context to generate a response. In reality, it’s a much more complex system involving workflows that invoke FMs, tools, and APIs and that use domain-specific data to ground responses with patterns such as Retrieval Augmented Generation (RAG) and workflows involving agents. Safety controls

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Insights in implementing production-ready solutions with generative AI

Artificial Intelligence, AWS Well-Architected Framework, Best Practices, Customer Solutions, Experience-Based Acceleration, Generative AI, Thought Leadership /

As generative AI revolutionizes industries, organizations are eager to harness its potential. However, the journey from production-ready solutions to full-scale implementation can present distinct operational and technical considerations. This post explores key insights and lessons learned from AWS customers in Europe, Middle East, and Africa (EMEA) who have successfully navigated this transition, providing a roadmap

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InterVision accelerates AI development using AWS LLM League and Amazon SageMaker AI

Amazon Machine Learning, Amazon SageMaker, Amazon SageMaker AI, Amazon SageMaker JumpStart, Amazon SageMaker Studio, Foundational (100), Partner solutions, Technical How-to, Thought Leadership /

Cities and local governments are continuously seeking ways to enhance their non-emergency services, recognizing that intelligent, scalable contact center solutions play a crucial role in improving citizen experiences. InterVision Systems, LLC (InterVision), an AWS Premier Tier Services Partner and Amazon Connect Service Delivery Partner, has been at the forefront of this transformation, with their contact

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Improve Amazon Nova migration performance with data-aware prompt optimization

Amazon Bedrock, Amazon Nova, Generative AI, Thought Leadership /

In the era of generative AI, new large language models (LLMs) are continually emerging, each with unique capabilities, architectures, and optimizations. Among these, Amazon Nova foundation models (FMs) deliver frontier intelligence and industry-leading cost-performance, available exclusively on Amazon Bedrock. Since its launch in 2024, generative AI practitioners, including the teams in Amazon, have started transitioning

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

Advanced (300), Amazon Bedrock Agents, Technical How-to, Thought Leadership /

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 you to gauge how well an agent is performing certain actions and gain key insights into

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Dynamic text-to-SQL for enterprise workloads with Amazon Bedrock Agents

Amazon Bedrock, Amazon Bedrock Agents, Best Practices, Generative AI, Technical How-to, Thought Leadership /

Generative AI enables us to accomplish more in less time. Text-to-SQL empowers people to explore data and draw insights using natural language, without requiring specialized database knowledge. Amazon Web Services (AWS) has helped many customers connect this text-to-SQL capability with their own data, which means more employees can generate insights. In this process, we discovered

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