Github Dus4w Ai Aws Generative Ai Use Cases Application

Bonisiwe Shabane

-Dec 28, 2025, 2:15 PM

github dus4w ai aws generative ai use cases application

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.

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. 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: 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. 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 There was an error while loading. Please reload this page. You can create a release to package software, along with release notes and links to binary files, for other people to use. Learn more about releases in our docs. LLMs and other foundation models have been pre-trained on a large corpus of data enabling them to perform well at many natural language processing (NLP) tasks.

But most foundation models and LLMs are static and have been pre-trained, limiting their ability to accurately answer questions on topics which are either new, specialized, or proprietary. Using prompt-based learning, you can leverage the powerful NLP and text generation features of an LLM to provide richer customer experiences over your enterprise data. Out of the box, the solution comes bundled with various model providers and use cases. With an easy to use deployment wizard, customers can deploy pre-built use cases to enable the rapid experimentation of different generative AI prototypes and workloads. Multi LLM comparison and experimentation LLMs perform differently, and given your application’s specific needs, you may find that one LLM suits your application better than another.

This may be for reasons related to performance, accuracy, cost, creativity, or many other factors. This solution lets you quickly deploy multiple use cases enabling you to experiment with and compare different configurations until you’ve found what meets your needs. Thanks for letting us know we're doing a good job! Build Generative AI applications on AWS: Complete guide Businesses have long faced challenges like manual content creation, high costs, slow innovation, and inefficient customer service. On top of that, data analysis bottlenecks make decision-making sluggish, impacting growth and competitiveness.

Generative AI changes the game by automating workflows, improving efficiency, and enhancing personalization. It helps businesses generate high-quality content, speed up development, and gain real-time insights—all while cutting costs. AWS is the ideal platform for generative AI development, offering scalability, security, and cost-efficient AI infrastructure. With pre-trained AI models, seamless integrations, and compliance-ready tools, businesses can build and deploy AI solutions without the hassle of managing complex infrastructure. This blog breaks down how to build generative AI applications on AWS, the key services to use, and cost-saving strategies. You’ll also find real-world use cases and expert solutions to common challenges.

Github Dus4w Ai Aws Generative Ai Use Cases Application

People Also Search

Well-architected Application Implementation With Business Use Cases For Utilizing Generative

We Plan To Continuously Add More Refined Use Cases In

Five Proven Ways To Turn AI Potential Into Real Results

Modern AI Tools Accelerate Coding, Improve Code Quality, And Enable

For Detailed Information About Specific Use Cases And Their Implementations,