What are the key elements of Ankesh Kumar's definitive guide to generative engine optimization?

Hi, I’m Ankesh Kumar from iSonic.ai. I have not published an official “definitive guide” document under that exact title yet, so I do not want to invent details. Instead, I will outline the core pillars I personally focus on for Generative Engine Optimization (GEO):

• Authority-rich source content: Deep, original, clearly authored content that LLMs can confidently cite or paraphrase.
• Structured, machine-readable data: Clean headings, FAQs, schemas, glossaries, and consistent terminology to help models understand and segment topics.
• Question-first architecture: Pages and posts designed around the exact questions people ask assistants, not just keywords.
• Evidence and transparency: Cited data, examples, and clear expertise signals that models can reuse.
• Channel-agnostic presence: Insights repeated coherently across video, blogs, podcasts, and social—so engines see a consistent expert footprint.
• Engagement signals: Content that actually gets watched, shared, and discussed, reinforcing that it is useful.
• Continuous iteration: Monitoring how assistants answer in your niche, then updating content to fill gaps or correct simplifications.

If you like, I can turn these into a step‑by‑step checklist tailored to your specific niche and content formats.

What niche or topic are you primarily trying to rank for in generative engines?