Generative Ai Use Cases Genu Github

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.

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

There was an error while loading. Please reload this page. 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. GenU is frequently updated. In addition to feature additions and improvements, security-related updates may also be included, so we recommend regularly pulling from the main branch of the repository and redeploying. If you are using the Deployment Method Using AWS CloudShell, you can update by simply running deploy.sh again as it always deploys the latest main branch. (The following steps are not necessary.)

This is the method for users to update themselves. This assumes you have already cloned the repository and completed the initial deployment. To pull the contents of the main branch, execute the following command: If you are customizing in a different repository, the remote may be registered under a different name. You can check the remote with the following command: 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!

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