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Syngenta develops a generative AI assistant to support sales representatives using Amazon Bedrock Agents

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This solution is also deployed by using the AWS Cloud Development Kit (AWS CDK), which is an open-source software development framework that defines cloud infrastructure in modern programming languages and provisions it through AWS CloudFormation.

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Reducing hallucinations in LLM agents with a verified semantic cache using Amazon Bedrock Knowledge Bases

AWS Machine Learning Blog

Similar to how a customer service team maintains a bank of carefully crafted answers to frequently asked questions (FAQs), our solution first checks if a users question matches curated and verified responses before letting the LLM generate a new answer. No LLM invocation needed, response in less than 1 second.

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Build agentic systems with CrewAI and Amazon Bedrock

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Consider a software development use case AI agents can generate, evaluate, and improve code, shifting software engineers focus from routine coding to more complex design challenges. Agentic systems, on the other hand, are designed to bridge this gap by combining the flexibility of context-aware systems with domain knowledge.

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Time series forecasting with LLM-based foundation models and scalable AIOps on AWS

AWS Machine Learning Blog

However, traditional machine learning approaches often require extensive data-specific tuning and model customization, resulting in lengthy and resource-heavy development. Enter Chronos , a cutting-edge family of time series models that uses the power of large language model ( LLM ) architectures to break through these hurdles.

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Evaluate large language models for your machine translation tasks on AWS

AWS Machine Learning Blog

However, the industry is seeing enough potential to consider LLMs as a valuable option. The following are a few potential benefits: Improved accuracy and consistency LLMs can benefit from the high-quality translations stored in TMs, which can help improve the overall accuracy and consistency of the translations produced by the LLM.

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Use Kubernetes Operators for new inference capabilities in Amazon SageMaker that reduce LLM deployment costs by 50% on average

AWS Machine Learning Blog

of the SageMaker ACK Operators adds support for inference components , which until now were only available through the SageMaker API and the AWS Software Development Kits (SDKs). These controllers allow Kubernetes users to provision AWS resources like buckets, databases, or message queues simply by using the Kubernetes API.

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Build a dynamic, role-based AI agent using Amazon Bedrock inline agents

AWS Machine Learning Blog

For this demo, weve implemented metadata filtering to retrieve only the appropriate level of documents based on the users access level, further enhancing efficiency and security. The role information is also used to configure metadata filtering in the knowledge bases to generate relevant responses.