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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. 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 This repository contains sample code demonstrating various use cases leveraging Amazon Bedrock and Generative AI.

Each sample is a separate project with its own directory, and includes a basic Streamlit frontend to help users quickly set up a proof of concept. Amazon Bedrock Alt Text Generator This POC demonstrates how to use the Amazon Bedrock Alt Text Generator to generate alt text for images in PDF documents. Amazon Bedrock Amazon Athena POC This is sample code demonstrating the use of Amazon Bedrock and Generative AI to use natural language questions to query relational data stores, specifically Amazon Athena. This example leverages the MOMA Open Source Database: https://github.com/MuseumofModernArt/collection. Amazon Bedrock & Amazon RDS POC This is sample code demonstrating the use of Amazon Bedrock and Generative AI to use natural language questions to query relational data stores, specifically Amazon RDS. This example leverages the MOMA Open Source Database: https://github.com/MuseumofModernArt/collection.

Amazon Bedrock & Amazon Redshift POC This is sample code demonstrating the use of Amazon Bedrock and Generative AI to use natural language questions to query relational data stores, specifically Amazon Redshift. This example leverages the MOMA Open Source Database: https://github.com/MuseumofModernArt/collection. This Guidance demonstrates how to build and deploy comprehensive generative AI applications on AWS using a complete enterprise architecture. It shows organizations how to integrate security, content delivery, and authentication services with powerful artificial intelligence and serverless computing capabilities. The solution helps teams implement secure, high-performance AI applications by combining robust frontend security controls with flexible backend processing and storage solutions. By incorporating optional components for intelligent search and knowledge retrieval, alongside comprehensive monitoring and scaling capabilities, this guidance enables organizations to create sophisticated AI solutions that meet enterprise requirements for security, performance, and reliability.

Deploy production-ready generative AI applications in days instead of months. Leverage pre-configured components with AWS managed services to reduce development cycles while maintaining enterprise-grade security and scalability. Transform how employees access organizational knowledge with intelligent search and retrieval. Combine Amazon Bedrock and Kendra to create context-aware responses that improve decision-making and reduce time spent searching for information. Build customer-facing AI applications that automatically adjust to demand without performance degradation. Implement comprehensive security controls at every layer while focusing on creating differentiated user experiences rather than infrastructure management.

These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step. 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.

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