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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.14215 |
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| _version_ | 1866910752063881216 |
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| author | Kohl, Jens Gloger, Luisa Costa, Rui Kruse, Otto Luitz, Manuel P. Katz, David Barbeito, Gonzalo Schweier, Markus French, Ryan Schroeder, Jonas Riedl, Thomas Perri, Raphael Mostafa, Youssef |
| author_facet | Kohl, Jens Gloger, Luisa Costa, Rui Kruse, Otto Luitz, Manuel P. Katz, David Barbeito, Gonzalo Schweier, Markus French, Ryan Schroeder, Jonas Riedl, Thomas Perri, Raphael Mostafa, Youssef |
| contents | As LLM-based applications reach millions of customers, ensuring their scalability and continuous quality improvement is critical for success. However, the current workflows for developing, maintaining, and operating (DevOps) these applications are predominantly manual, slow, and based on trial-and-error. With this paper we introduce the Generative AI Toolkit, which automates essential workflows over the whole life cycle of LLM-based applications. The toolkit helps to configure, test, continuously monitor and optimize Generative AI applications such as agents, thus significantly improving quality while shortening release cycles. We showcase the effectiveness of our toolkit on representative use cases, share best practices, and outline future enhancements. Since we are convinced that our Generative AI Toolkit is helpful for other teams, we are open sourcing it on and hope that others will use, forward, adapt and improve |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_14215 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generative AI Toolkit -- a framework for increasing the quality of LLM-based applications over their whole life cycle Kohl, Jens Gloger, Luisa Costa, Rui Kruse, Otto Luitz, Manuel P. Katz, David Barbeito, Gonzalo Schweier, Markus French, Ryan Schroeder, Jonas Riedl, Thomas Perri, Raphael Mostafa, Youssef Software Engineering Artificial Intelligence I.2.7; I.2.11 As LLM-based applications reach millions of customers, ensuring their scalability and continuous quality improvement is critical for success. However, the current workflows for developing, maintaining, and operating (DevOps) these applications are predominantly manual, slow, and based on trial-and-error. With this paper we introduce the Generative AI Toolkit, which automates essential workflows over the whole life cycle of LLM-based applications. The toolkit helps to configure, test, continuously monitor and optimize Generative AI applications such as agents, thus significantly improving quality while shortening release cycles. We showcase the effectiveness of our toolkit on representative use cases, share best practices, and outline future enhancements. Since we are convinced that our Generative AI Toolkit is helpful for other teams, we are open sourcing it on and hope that others will use, forward, adapt and improve |
| title | Generative AI Toolkit -- a framework for increasing the quality of LLM-based applications over their whole life cycle |
| topic | Software Engineering Artificial Intelligence I.2.7; I.2.11 |
| url | https://arxiv.org/abs/2412.14215 |