Explore the process of conversational code generation using Large Language Models (LLMs) in this 10-minute video tutorial. Learn how to refine and iterate on code prompts, interactively request changes to generated code, and consolidate conversational history into a single prompt for maintainable code generation in complex projects. Dive into practical examples, such as building a PyTorch neural network to approximate the XOR function, and discover how to guide LLMs with follow-up prompts to fine-tune models for better results. Gain insights into best practices for formatting and structuring prompts, ensuring your final code meets industry standards like PEP-8, and includes well-structured comments and sorted imports.