用可解释的LLM框架实现旅游定价自动化，兼顾灵活性与可靠性。
Natural-Language Policies to Executable Decisions: An Interpretable Large Language Model Framework
- LLM提取结构化信息并选择政策路径，数值计算全程确定性执行
- 政策编译为可读条件树，支持规则动态更新且无需改代码
- 已在多地景区落地，人力减少80%，每单处理时间缩短至2分钟内
大规模旅游定价自动化面临挑战：订单信息高度非结构化，定价策略复杂多变且开放。传统规则引擎脆弱难维护，而无约束的LLM代理缺乏金融决策所需的可靠性和可审计性。本文提出一个生产级的LLM驱动定价系统，设定严格决策边界：LLM负责结构化信息抽取与受限策略/路径选择，所有数值计算（包括总价）均确定性执行。策略被编译为可解释的条件树，支持新条款和规则演进而无需代码变更，同时生成可审计的中间产物以支持人工介入。通过定期在日志轨迹上微调，提升条件树生成与路径匹配精度。该系统部署于一家国有旅游企业，覆盖7个景区、12类业务，服务1500+操作员和1000+活跃策略，六个月内处理3960个订单，订单管理团队从15-20人缩减至3人，单订单处理时间由10分钟降至<2分钟。
原文摘要 · Abstract (English)
Pricing automation in large-scale tourism is challenging because travel orders are highly unstructured, while pricing policies are complex, rapidly evolving, and inherently open-ended. Traditional rule engines are brittle and costly to maintain, whereas unconstrained LLM agents lack the reliability and auditability required for financial decisions. We present a production-grade LLM-powered pricing system with a strict decision boundary: LLMs perform structured extraction and bounded policy/path selection, while all numeric pricing, including total-price computation, is executed deterministically. Policies are compiled into interpretable condition trees, enabling open-ended support for new clauses and evolving rules without code changes, while exposing auditable artifacts for human-in-the-loop control. Periodic fine-tuning on logged traces further improves tree induction and path matching. Deployed at a municipal state-owned tourism enterprise across 7 scenic sites and 12 business categories with 1,500+ operators and 1,000+ active policies, the system processed 3,960 orders in six months, reduced the order management team from 15-20 to 3, and cut per-order handling time from 10 minutes to <2 minutes.
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