The article discusses the potential of large language models (LLMs) to democratize climate information by providing localized and actionable climate data. The authors, Nikolay Koldunov and Thomas Jung, present ClimSight, a prototype tool that combines LLMs with geographical and climate data to offer insights on climate change impacts for specific locations. ClimSight can assess the risks and benefits of various activities, such as wheat cultivation, wind farm installation, and crop selection, based on future climate predictions. The tool is designed to be cost-effective and accessible, with queries costing approximately 6 cents using OpenAI GPT-4 API. The authors highlight the need for further development, including expanding data resolution, integrating user-relevant information, and ensuring the reliability and transparency of the system's outputs. They also emphasize the importance of open-source models to democratize the development and improvement of such systems.The article discusses the potential of large language models (LLMs) to democratize climate information by providing localized and actionable climate data. The authors, Nikolay Koldunov and Thomas Jung, present ClimSight, a prototype tool that combines LLMs with geographical and climate data to offer insights on climate change impacts for specific locations. ClimSight can assess the risks and benefits of various activities, such as wheat cultivation, wind farm installation, and crop selection, based on future climate predictions. The tool is designed to be cost-effective and accessible, with queries costing approximately 6 cents using OpenAI GPT-4 API. The authors highlight the need for further development, including expanding data resolution, integrating user-relevant information, and ensuring the reliability and transparency of the system's outputs. They also emphasize the importance of open-source models to democratize the development and improvement of such systems.