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Well-architected application implementation with business use cases for utilizing generative AI in business operations
[!IMPORTANT]
GenU has supported multiple languages since v4.
GenU は v4 から多言語対応しました。日本語ドキュメントはこちら
GenU Usage Patterns
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
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.
GenU provides a RAG Chat use case.
Two types of information sources are available for RAG Chat: Amazon Kendra and Knowledge Base.
When using Amazon Kendra, you can use manually created S3 Buckets or Kendra Indexes as they are.
When using Knowledge Base, advanced RAG features such as Advanced Parsing, Chunk Strategy Selection, Query Decomposition, and Reranking are available.
Knowledge Base also allows for Metadata Filter Settings.
For example, you can meet requirements such as "switching accessible data sources by organization" or "allowing users to set filters from the UI."
Additionally, it is possible to build a RAG that references data outside of AWS by enabling MCP chat and adding an external service's MCP server to packages/cdk/mcp-api/mcp.json.
When you enable agents in GenU, Web Search Agent and Code Interpreter Agent are created.
The Web Search Agent searches the web for information to answer user questions. For example, it can answer "What is AWS GenU?"
The Code Interpreter Agent can execute code to respond to user requests. For example, it can respond to requests like "Draw a scatter plot with some dummy data."
While Web Search Agent and Code Interpreter Agent are basic agents, you might want to use more practical agents tailored to your business needs.
GenU provides a feature to import agents that you've created manually or with other assets.
By using GenU as a platform for agent utilization, you can leverage GenU's rich security options and SAML authentication to spread practical agents within your organization.
Additionally, you can hide unnecessary standard use cases or display agents inline to use GenU as a more agent-focused platform.
Similarly, there is an import feature for AgentCore Runtime, so please make use of it.
Similarly, there is an import feature for Bedrock Flows, so please make use of it.
Additionally, you can create agents that perform actions on services outside AWS by enabling MCP chat and adding external MCP servers to packages/cdk/mcp-api/mcp.json.
GenU provides a feature called "Use Case Builder" that allows you to create custom use cases by describing prompt templates in natural language.
Custom use case screens are automatically generated just from prompt templates, so no code changes to GenU itself are required.
Created use cases can be shared with all users who can log into the application, not just for personal use.
Use Case Builder can be disabled if not needed.
Use cases can also be exported as .json files and shared with third parties. When sharing use cases, please be careful not to include any confidential information in prompts or usage examples. Use cases shared by third parties can be imported by uploading the .json file from the new use case creation screen.
For more details about Use Case Builder, please check this blog.
While Use Case Builder can create use cases where you input text into forms or attach files, depending on your requirements, a chat UI might be more suitable.
In such cases, please utilize the system prompt saving feature of the "Chat" use case.
By saving system prompts, you can create business-necessary "bots" with just one click.
For example, you can create "a bot that thoroughly reviews source code when input" or "a bot that extracts email addresses from input content."
Additionally, chat conversation histories can be shared with logged-in users, and system prompts can be imported from shared conversation histories.
Since GenU is OSS, you can also customize it to add your own use cases.
In that case, please be careful about conflicts with GenU's main branch.
Deployment
[!IMPORTANT]
Please enable the modelIds (text generation), imageGenerationModelIds (image generation), and videoGenerationModelIds (video generation) in the modelRegion region listed in /packages/cdk/cdk.json. (Amazon Bedrock Model access screen)
GenU deployment uses AWS Cloud Development Kit (CDK). If you cannot prepare a CDK execution environment, refer to the following deployment methods:
First, run the following command. All commands should be executed at the repository root.
npm ci
If you've never used CDK before, you need to Bootstrap for the first time only. The following command is unnecessary if your environment is already bootstrapped.
npx -w packages/cdk cdk bootstrap
Next, deploy AWS resources with the following command. Please wait for the deployment to complete (it may take about 20 minutes).
# Normal deployment
npm run cdk:deploy
# Fast deployment (quickly deploy without pre-checking created resources)
npm run cdk:deploy:quick
Architecture
Other Information
Cost Estimation
We have published configuration and cost estimation examples for using GenU. (The service is pay-as-you-go, and actual costs will vary depending on your usage.)
Customer Case Studies
| Customer | Quote |
|---|
| Yasashiite Co., Ltd. Thanks to GenU, we were able to provide added value to users and improve employee work efficiency. We continue to evolve from "smooth operation" to "exciting work" as employees' "previous work" transforms into enjoyable work! ・See case details ・See case page |
| TAKIHYO Co., Ltd. Achieved internal business efficiency and reduced over 450 hours of work by utilizing generative AI. Applied Amazon Bedrock to clothing design, etc., and promoted digital talent development. ・See case page |
| Salsonido Inc. By utilizing GenU, which is provided as a solution, we were able to quickly start improving business processes with generative AI. ・See case details ・Applied service |
| TAMURA CORPORATION The application samples that AWS publishes on Github have a wealth of immediately testable functions, and by using them as they are, we were able to easily select functions that suited us and shorten the development time of the final system. ・See case details |
| JDSC Inc. Amazon Bedrock allows us to securely use LLMs with our data. Also, we can switch to the optimal model depending on the purpose, allowing us to improve speed and accuracy while keeping costs down. ・See case details |
If you would like to have your use case featured, please contact us via Issue.
References
Security
See CONTRIBUTING for more information.
License
This library is licensed under the MIT-0 License. See the LICENSE file.
