Jason Gilman from Element 84 discusses the integration of large language models (LLMs) with geospatial data to enhance search and analysis capabilities in his talk at FOSS4G NA 2024. Highlights 🌍 LLMs can bridge the gap between geospatial data and user inquiries, enabling effective search. 🤖 LLMs function like CPUs, processing natural language but lacking real-world awareness. 🌐 A “broker” system is essential to manage LLM’s capabilities and ensure deterministic outputs. 📊 The use of JSON and vector databases facilitates efficient data extraction and manipulation. 🗺️ Natural language geocoding allows users to specify geospatial queries easily. 💻 LLMs can generate SQL queries from natural language, streamlining database interactions. ⚡ Performance optimization is crucial, balancing prompt brevity with output quality. For more content like this check out www.projectgeospatial.com #Geospatial #AI #LLM #DataAnalysis #FOSS4G #NaturalLanguageProcessing #TechInnovation