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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2605.17809 |
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| _version_ | 1866916024218025984 |
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| author | Tseng, Chun-Hsiung Lin, Hao-Chiang Koong Huang, Andrew Chih-Wei Chen, Yung-Hui Lin, Jia-Rou |
| author_facet | Tseng, Chun-Hsiung Lin, Hao-Chiang Koong Huang, Andrew Chih-Wei Chen, Yung-Hui Lin, Jia-Rou |
| contents | This research addresses the challenges inherent in developing Artificial Intelligence (AI) applications, particularly those leveraging Large Language Models (LLMs). While AI vendors provide Application Programming Interfaces (APIs) and Software Development Kits (SDKs) to facilitate developer interaction, the former often requires intricate manual request construction, and the latter can lead to significant vendor lock-in. Furthermore, existing model abstraction frameworks, though mitigating vendor dependency, introduce an additional layer of complexity and potential security concerns. To reconcile these conflicting factors, the study introduces PuppyChatter, a novel software framework designed to preserve the intuitive simplicity of vendor-specific SDKs while simultaneously adhering to the vendor-neutrality principles characteristic of model abstraction, thereby offering a more streamlined and flexible development paradigm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_17809 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Accelerating AI-Powered Research: The PuppyChatter Framework for Usable and Flexible Tooling Tseng, Chun-Hsiung Lin, Hao-Chiang Koong Huang, Andrew Chih-Wei Chen, Yung-Hui Lin, Jia-Rou Artificial Intelligence Information Retrieval This research addresses the challenges inherent in developing Artificial Intelligence (AI) applications, particularly those leveraging Large Language Models (LLMs). While AI vendors provide Application Programming Interfaces (APIs) and Software Development Kits (SDKs) to facilitate developer interaction, the former often requires intricate manual request construction, and the latter can lead to significant vendor lock-in. Furthermore, existing model abstraction frameworks, though mitigating vendor dependency, introduce an additional layer of complexity and potential security concerns. To reconcile these conflicting factors, the study introduces PuppyChatter, a novel software framework designed to preserve the intuitive simplicity of vendor-specific SDKs while simultaneously adhering to the vendor-neutrality principles characteristic of model abstraction, thereby offering a more streamlined and flexible development paradigm. |
| title | Accelerating AI-Powered Research: The PuppyChatter Framework for Usable and Flexible Tooling |
| topic | Artificial Intelligence Information Retrieval |
| url | https://arxiv.org/abs/2605.17809 |