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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2506.20815 |
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| _version_ | 1866912471296507904 |
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| author | Tang, Xinye Zhai, Haijun Belwal, Chaitanya Thayanithi, Vineeth Baumann, Philip Roy, Yogesh K |
| author_facet | Tang, Xinye Zhai, Haijun Belwal, Chaitanya Thayanithi, Vineeth Baumann, Philip Roy, Yogesh K |
| contents | LLM-powered applications are highly susceptible to the quality of user prompts, and crafting high-quality prompts can often be challenging especially for domain-specific applications. This paper presents a novel dynamic context-aware prompt recommendation system for domain-specific AI applications. Our solution combines contextual query analysis, retrieval-augmented knowledge grounding, hierarchical skill organization, and adaptive skill ranking to generate relevant and actionable prompt suggestions.
The system leverages behavioral telemetry and a two-stage hierarchical reasoning process to dynamically select and rank relevant skills, and synthesizes prompts using both predefined and adaptive templates enhanced with few-shot learning. Experiments on real-world datasets demonstrate that our approach achieves high usefulness and relevance, as validated by both automated and expert evaluations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_20815 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dynamic Context-Aware Prompt Recommendation for Domain-Specific AI Applications Tang, Xinye Zhai, Haijun Belwal, Chaitanya Thayanithi, Vineeth Baumann, Philip Roy, Yogesh K Artificial Intelligence LLM-powered applications are highly susceptible to the quality of user prompts, and crafting high-quality prompts can often be challenging especially for domain-specific applications. This paper presents a novel dynamic context-aware prompt recommendation system for domain-specific AI applications. Our solution combines contextual query analysis, retrieval-augmented knowledge grounding, hierarchical skill organization, and adaptive skill ranking to generate relevant and actionable prompt suggestions. The system leverages behavioral telemetry and a two-stage hierarchical reasoning process to dynamically select and rank relevant skills, and synthesizes prompts using both predefined and adaptive templates enhanced with few-shot learning. Experiments on real-world datasets demonstrate that our approach achieves high usefulness and relevance, as validated by both automated and expert evaluations. |
| title | Dynamic Context-Aware Prompt Recommendation for Domain-Specific AI Applications |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2506.20815 |