Imagine effortlessly transforming complex geospatial analysis requests into functional code. That's the promise of automated code generation using Large Language Models (LLMs). But a significant hurdle stands in the way: code hallucination, where LLMs generate nonsensical or non-executable code. A new research paper proposes a clever solution: the Chain-of-Programming (CoP) framework. This innovative approach breaks down the code generation process into five distinct steps, mimicking a human programmer's workflow. It starts with thorough requirement analysis, ensuring the LLM understands the task's specifics, like the platform, programming language, data source, and desired output. Then, CoP guides the LLM through algorithm design, code implementation, debugging, and even annotation, all while maintaining a shared information pool to ensure consistency. Think of it as giving the LLM a structured thought process. To combat the LLM's lack of specialized geospatial knowledge, CoP incorporates knowledge base retrieval. This allows the model to access relevant platform syntax, function libraries, and built-in datasets on demand. Moreover, CoP integrates expert feedback to refine the code, making it more accurate and readable. The researchers tested CoP on various LLMs, including commercial giants like GPT-4 and open-source models like Code Llama. The results? Significant improvements across the board. Matchability (how well the code aligns with the request) soared, executability improved dramatically, and even code readability got a boost. Case studies demonstrate CoP's practical application. One example used Python to map building distributions in Manhattan, while another leveraged Google Earth Engine to analyze burned areas in Henan Province, China. These examples showcase CoP's versatility in handling diverse geospatial tasks, both locally and on the cloud. While CoP demonstrates a substantial leap forward, the research team acknowledges room for improvement. Optimizing the knowledge base for broader compatibility and automating the feedback mechanism are key next steps. The ultimate goal? A future where anyone, regardless of programming expertise, can harness the power of geospatial analysis with simple, natural language requests.