用自然语言设计对话系统，让业务专家也能高效构建复杂流程。
Conversation Routines: A Prompt Engineering Framework for Task-Oriented Dialog Systems
- 将任务逻辑嵌入提示词，通过自然语言定义对话流程。
- 在订票与故障排查系统中验证，保持对话灵活性同时实现精准执行。
- 适合希望快速迭代的业务专家与开发者协同开发场景。
本文提出对话例行程序（Conversation Routines, CR），一种基于大语言模型（LLM）的任务导向对话系统提示工程框架。尽管LLM具备出色的语言理解能力，但将其可靠用于复杂业务流程仍具挑战。CR通过自然语言规范，将任务逻辑嵌入提示词，支持开发对话智能体系统（CAS），实现复杂对话流程的系统化设计与一致行为。我们以火车票预订系统和交互式故障排查助手为案例，验证了该框架在编码复杂行为模式与决策逻辑方面的有效性，同时保持自然对话灵活性。结果表明，领域专家可通过自然语言设计对话流程，软件工程师则专注于自定义函数（工具）实现，形成高效分工。尽管框架在可访问性与适应性方面表现良好，仍面临计算开销大、行为非确定性及领域逻辑优化等挑战。未来研究方向包括基于目标导向评分标准的框架评估方法、复杂多智能体交互的可扩展性提升，以及增强系统鲁棒性以应对多样商业应用中的局限性。
原文摘要 · Abstract (English)
This study introduces Conversation Routines (CR), a structured prompt engineering framework for developing task-oriented dialog systems using Large Language Models (LLMs). While LLMs demonstrate remarkable natural language understanding capabilities, engineering them to reliably execute complex business workflows remains challenging. The proposed CR framework enables the development of Conversation Agentic Systems (CAS) through natural language specifications, embedding task-oriented logic within LLM prompts. This approach provides a systematic methodology for designing and implementing complex conversational workflows while maintaining behavioral consistency. We demonstrate the framework's effectiveness through two proof-of-concept implementations: a Train Ticket Booking System and an Interactive Troubleshooting Copilot. These case studies validate CR's capability to encode sophisticated behavioral patterns and decision logic while preserving natural conversational flexibility. Results show that CR enables domain experts to design conversational workflows in natural language while leveraging custom functions (tools) developed by software engineers, creating an efficient division of responsibilities where developers focus on core API implementation and domain experts handle conversation design. While the framework shows promise in accessibility and adaptability, we identify key challenges including computational overhead, non-deterministic behavior, and domain-specific logic optimization. Future research directions include CR evaluation methods based on prompt engineering frameworks driven by goal-oriented grading criteria, improving scalability for complex multi-agent interactions, and enhancing system robustness to address the identified limitations across diverse business applications.
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