This contribution introduces a method for generating long-form content using a recursive prompting technique. Instead of asking for a full article at once, the user guides the AI to build a "mental map" of the topic first, then expands each node iteratively. This ensures consistency and depth often lost in single-shot prompts. In this Hub, the community can test this method across different LLMs (GPT-4, Claude 3.5, Gemini) to see which maintains context longest. Members are encouraged to share their "Branching Logic" templates. This turns the Hub into a repository for high-fidelity content creation that bypasses the "AI-generated" look.