The Hitchhikers Guide to Production-ready Trustworthy Foundation Model powered Software (FMware)
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arXiv
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| Autores principales: | , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866909634480046080 |
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| author | Vasilevski, Kirill Rombaut, Benjamin Rajbahadur, Gopi Krishnan Oliva, Gustavo A. Gallaba, Keheliya Cogo, Filipe R. Lin, Jiahuei Lin, Dayi Zhang, Haoxiang Chen, Bouyan Thangarajah, Kishanthan Hassan, Ahmed E. Jiang, Zhen Ming |
| author_facet | Vasilevski, Kirill Rombaut, Benjamin Rajbahadur, Gopi Krishnan Oliva, Gustavo A. Gallaba, Keheliya Cogo, Filipe R. Lin, Jiahuei Lin, Dayi Zhang, Haoxiang Chen, Bouyan Thangarajah, Kishanthan Hassan, Ahmed E. Jiang, Zhen Ming |
| contents | Foundation Models (FMs) such as Large Language Models (LLMs) are reshaping the software industry by enabling FMware, systems that integrate these FMs as core components. In this KDD 2025 tutorial, we present a comprehensive exploration of FMware that combines a curated catalogue of challenges with real-world production concerns. We first discuss the state of research and practice in building FMware. We further examine the difficulties in selecting suitable models, aligning high-quality domain-specific data, engineering robust prompts, and orchestrating autonomous agents. We then address the complex journey from impressive demos to production-ready systems by outlining issues in system testing, optimization, deployment, and integration with legacy software. Drawing on our industrial experience and recent research in the area, we provide actionable insights and a technology roadmap for overcoming these challenges. Attendees will gain practical strategies to enable the creation of trustworthy FMware in the evolving technology landscape. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_10640 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Hitchhikers Guide to Production-ready Trustworthy Foundation Model powered Software (FMware) Vasilevski, Kirill Rombaut, Benjamin Rajbahadur, Gopi Krishnan Oliva, Gustavo A. Gallaba, Keheliya Cogo, Filipe R. Lin, Jiahuei Lin, Dayi Zhang, Haoxiang Chen, Bouyan Thangarajah, Kishanthan Hassan, Ahmed E. Jiang, Zhen Ming Software Engineering Artificial Intelligence Machine Learning Foundation Models (FMs) such as Large Language Models (LLMs) are reshaping the software industry by enabling FMware, systems that integrate these FMs as core components. In this KDD 2025 tutorial, we present a comprehensive exploration of FMware that combines a curated catalogue of challenges with real-world production concerns. We first discuss the state of research and practice in building FMware. We further examine the difficulties in selecting suitable models, aligning high-quality domain-specific data, engineering robust prompts, and orchestrating autonomous agents. We then address the complex journey from impressive demos to production-ready systems by outlining issues in system testing, optimization, deployment, and integration with legacy software. Drawing on our industrial experience and recent research in the area, we provide actionable insights and a technology roadmap for overcoming these challenges. Attendees will gain practical strategies to enable the creation of trustworthy FMware in the evolving technology landscape. |
| title | The Hitchhikers Guide to Production-ready Trustworthy Foundation Model powered Software (FMware) |
| topic | Software Engineering Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2505.10640 |