Generative Ai Use Cases Genu Aws Samples Github Io

There was an error while loading. Please reload this page. This document introduces GenU (Generative AI Use Cases), a well-architected serverless platform for deploying and managing generative AI business applications on AWS. It covers the high-level system architecture, key components, and the platform's extensibility model. For detailed information about specific use cases and their implementations, see System Features and Use Cases. For the monorepo structure and package dependencies, see Packages and Monorepo Structure.

For detailed architectural patterns, see Architecture. GenU (Generative AI Use Cases) is an open-source, production-ready platform that provides a collection of business-focused generative AI applications built on AWS services. The platform is designed to help organizations safely and effectively deploy generative AI capabilities in business operations. The platform leverages Amazon Bedrock as its primary AI service, providing access to foundation models from Anthropic (Claude), Amazon (Nova), and other providers without managing model infrastructure. GenU implements a three-tier serverless architecture deployed using AWS CDK. The following diagram shows the relationship between major system components and their corresponding code entities:

An open API service indexing awesome lists of open source software. Application implementation with business use cases for safely utilizing generative AI in business operations https://github.com/aws-samples/generative-ai-use-cases aws bedrock chatbot claude claude3 command-r deepseek-r1 generative-ai image-generation lambda llama3 llm mistral nova rag react sagemaker typescript Last synced: about 1 month ago JSON representation Application implementation with business use cases for safely utilizing generative AI in business operations Five proven ways to turn AI potential into real results

Transform your business with AWS — where great technology is designed to be easy to understand and solve real business challenges. Whether you're automating processes, building with AI-powered tools, creating compelling content, making faster data-driven decisions, or delivering personalized customer experiences — AWS provides the clarity and capabilities to turn AI potential into measurable business... Transform your business operations by eliminating repetitive, time-consuming processes that drain productivity and resources. AI-powered automation enables organizations to free human talent for high-value innovation and strategic work, delivering significant efficiency gains while reducing operational costs and human error across enterprise workflows. Revolutionize how development teams create, deploy, and maintain software by integrating AI capabilities directly into the development lifecycle. Modern AI tools accelerate coding, improve code quality, and enable developers to focus on innovation rather than routine programming tasks.

This transformation empowers teams to deliver applications faster while maintaining security and reliability standards. Speed up content creation across all formats by leveraging AI to transform concepts into polished, engaging materials that resonate with target audiences. AI-powered creative tools enable teams to produce high-quality content at scale while maintaining brand consistency and creative standards, democratizing content creation and enabling rapid iteration. Log in to subscribe to ecosystem digests, manage your profile and more. Well-architected application implementation with business use cases for utilizing generative AI in business operations [!IMPORTANT] GenU has supported multiple languages since v4.

Here we introduce GenU's features and options by usage pattern. For comprehensive deployment options, please refer to this document. [!TIP] Click on a usage pattern to see details Well-architected application implementation with business use cases for utilizing generative AI in business operations GenU has supported multiple languages since v4. Here we introduce GenU's features and options by usage pattern.

For comprehensive deployment options, please refer to this document. GenU provides a variety of standard use cases leveraging generative AI. These use cases can serve as seeds for ideas on how to utilize generative AI in business operations, or they can be directly applied to business as-is. We plan to continuously add more refined use cases in the future. If unnecessary, you can also hide specific use cases with an option. Here are the use cases provided by default.

RAG is a technique that allows LLMs to answer questions they normally couldn't by providing external up-to-date information or domain knowledge that LLMs typically struggle with. PDF, Word, Excel, and other files accumulated within your organization can serve as information sources. RAG also has the effect of preventing LLMs from providing "plausible but incorrect information" by only allowing answers based on evidence. Well-architected application implementation with business use cases for utilizing generative AI in business operations GenU has supported multiple languages since v4. Here we introduce GenU's features and options by usage pattern.

For comprehensive deployment options, please refer to this document. GenU provides a variety of standard use cases leveraging generative AI. These use cases can serve as seeds for ideas on how to utilize generative AI in business operations, or they can be directly applied to business as-is. We plan to continuously add more refined use cases in the future. If unnecessary, you can also hide specific use cases with an option. Here are the use cases provided by default.

RAG is a technique that allows LLMs to answer questions they normally couldn't by providing external up-to-date information or domain knowledge that LLMs typically struggle with. PDF, Word, Excel, and other files accumulated within your organization can serve as information sources. RAG also has the effect of preventing LLMs from providing "plausible but incorrect information" by only allowing answers based on evidence. Well-architected application implementation with business use cases for utilizing generative AI in business operations GenU has supported multiple languages since v4. Here we introduce GenU's features and options by usage pattern.

For comprehensive deployment options, please refer to this document. GenU provides a variety of standard use cases leveraging generative AI. These use cases can serve as seeds for ideas on how to utilize generative AI in business operations, or they can be directly applied to business as-is. We plan to continuously add more refined use cases in the future. If unnecessary, you can also hide specific use cases with an option. Here are the use cases provided by default.

RAG is a technique that allows LLMs to answer questions they normally couldn't by providing external up-to-date information or domain knowledge that LLMs typically struggle with. PDF, Word, Excel, and other files accumulated within your organization can serve as information sources. RAG also has the effect of preventing LLMs from providing "plausible but incorrect information" by only allowing answers based on evidence. There was an error while loading. Please reload this page.

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