智能推荐领域专用提示，提升大模型应用的使用效果
Dynamic Context-Aware Prompt Recommendation for Domain-Specific AI Applications
- 基于上下文动态分析与技能层级结构推荐提示
- 在真实数据集上验证，提示相关性和实用性显著提升
- 适合需要精准提示生成的行业应用开发者
大语言模型驱动的应用高度依赖用户提示质量，尤其在领域专用场景下，高质量提示的构建尤为困难。本文提出一种面向领域专用AI应用的动态上下文感知提示推荐系统。该系统结合上下文查询分析、检索增强知识定位、层次化技能组织与自适应技能排序，生成相关且可操作的提示建议。通过行为遥测数据与两阶段层次推理机制，动态选择并排序相关技能，并利用预设与自适应模板，结合少样本学习合成提示。在真实世界数据集上的实验表明，该方法在自动化与专家评估中均表现出高实用性与相关性。
原文摘要 · Abstract (English)
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.
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