Introduction to the Generative AI Use Cases JP Project
Generative AI is transforming businesses with innovative possibilities, and the Generative AI Use Cases JP (GenU) project aims to safely integrate these technologies into business operations. This project provides a comprehensive collection of business use cases along with practical application implementations.
Use Cases Overview
The GenU project includes a variety of use cases that demonstrate how businesses can benefit from generative AI technologies. New use cases are regularly added, and users can suggest additional scenarios by submitting an issue on the project's GitHub repository.
Chat
The chat use case allows users to interact with large language models (LLMs) in a conversational format. This platform facilitates quick adaptation to detailed or new use cases. It serves as an effective environment for prompt engineering verification.
RAG Chat
RAG, or Retrieval-Augmented Generation, enhances LLMs by incorporating external, domain-specific knowledge to provide accurate and evidence-based responses, avoiding misleading information. An example use case is automating internal inquiries through LLMs accessing corporate documents.
Agent Chat
By linking LLMs to APIs, the Agent Chat use case enables various tasks. A sample implementation uses a search engine to research and deliver necessary information as answers.
Prompt Flow Chat
Using Amazon Bedrock Prompt Flows, workflows are created by connecting prompts, foundational models, and other AWS services. The Prompt Flow Chat use case allows executing these workflows through conversations.
Text Generation
LLMs excel at generating text across different contexts, such as articles, reports, and emails. This use case showcases one of their strongest capabilities.
Summarization
LLMs can handle the summarization of vast amounts of text effectively, extracting necessary information interactively and offering summaries with context-based queries.
Proofreading
Beyond spotting typos, LLMs suggest improvements from an objective standpoint, factoring in text flow and content quality. This enhances document quality before human review.
Translation
Trained on multiple languages, LLMs offer translation services with contextual nuances, such as formality and target audience adaptations.
Web Content Extraction
This use case involves extracting web content like blogs and documents, refining them into coherent text. Extracted content can also be used in other scenarios like summarization and translation.
Image Generation
Generative AI can produce new images based on text or initial images, helping visualize ideas for design tasks efficiently, with prompt support from LLMs.
Video Analysis
Through multimodal models, both images and text inputs are analyzed. This feature requests analysis of video frames and text from LLMs.
Architecture
GenU uses a robust architecture: React for the front-end, with static files served via Amazon CloudFront and Amazon S3. The backend employs Amazon API Gateway and AWS Lambda, with authentication through Amazon Cognito. The backbone LLMs are from Amazon Bedrock, and Amazon Kendra is the RAG data source.
Deployment
GenU is deployed using the AWS Cloud Development Kit (CDK). For a step-by-step guide or alternative deployment methods, refer to the following resources:
Execute the following command for deployment:
npm ci
If using CDK for the first time, perform the Bootstrap step:
npx -w packages/cdk cdk bootstrap
Deploy AWS resources with:
npm run cdk:deploy
Deployment Options
Options include altering models, enabling specific use cases like RAG chat and Agent chat, and configuring security and cost settings. Detailed guides for these processes are available in the project's documentation.
Case Studies
Yasashii Te Co., Ltd.
Utilizing GenU, Yasashii Te streamlined caregiving report tasks, automating record-keeping and generating care plans from audio conversations.
Salsonido Co., Ltd.
By leveraging GenU for article writing, Salsonido addressed challenges in time, manpower, and expertise, significantly reducing rewriting efforts.
Conclusion
GenU demonstrates the transformative impact of generative AI on business operations, offering varied use cases and a powerful, scalable architecture. This project is designed to be a flexible tool for both current and future AI-driven solutions, ensuring businesses can fully benefit from advancements in artificial intelligence.