This study investigates the use of large language models to enhance the policymaking process. We first analyze planning-related job postings to revisit the evolving roles of planners in the era of AI. We then examine climate equity plans across the U.S. and apply ChatGPT to conduct semantic analysis, extracting policy, strategy, and action items related to transportation and energy. The methodological framework relied on a LangChain-native retrieval-augmented generation pipeline. Based on these extracted elements and their evaluated presence, we develop a content-based recommendation system to support cross-city policy comparison. The results indicate that, despite growing attention to AI, planning jobs largely retain their traditional domain emphases in transportation, environmental planning, housing, and land use. Communicative responsibilities remain central to planning practice. Climate equity plans commonly address transportation, environmental, and energy-related measures aimed at reducing greenhouse gas emissions and predominantly employ affirmative language. The demonstration of the recommendation system illustrates how planners can efficiently identify cities with similar policy practices, revealing patterns of geographic similarity in policy adoption. The study concludes by envisioning localized yet personalized AI-assisted systems that can be adapted within urban systems.

翻译：本研究探讨利用大语言模型优化政策制定流程。首先通过分析规划类职位招聘信息，重新审视人工智能时代规划师角色的演变趋势。随后系统考察美国各地的气候公平计划，运用ChatGPT进行语义分析，提取与交通和能源相关的政策、战略及行动措施。方法框架采用基于LangChain的检索增强生成技术路线。基于提取的政策要素及其评估结果，我们开发了基于内容的推荐系统以支持跨城市政策比较。研究结果表明：尽管人工智能关注度持续提升，规划职位仍主要保持交通、环境规划、住房与土地利用等传统领域侧重；沟通协调职责始终是规划实践的核心环节。气候公平计划普遍涵盖交通、环境与能源领域措施，旨在减少温室气体排放，且多采用积极表述范式。推荐系统的实证演示展示了规划师如何高效识别具有相似政策实践的城市，揭示了政策采纳过程中的地理相似性规律。研究最后展望了可在城市系统中部署的本地化、个性化人工智能辅助系统的发展前景。