Be a resolvable entity
One canonical author page, consistent naming across pen names, structured Person data, and external identifiers such as ORCID and store listings — so a machine can tell which Robert Shumake wrote which book.
AI in publishing
Publish resolvable entity data, machine-readable maps such as llms.txt and a sitemap, and long-form sourced answers on your own domain — so the model retrieves your words instead of paraphrasing someone's summary of them.
Search sent readers a link. Answer engines send them a sentence. For authors that changes the unit of visibility from the ranked page to the quotable passage.
One canonical author page, consistent naming across pen names, structured Person data, and external identifiers such as ORCID and store listings — so a machine can tell which Robert Shumake wrote which book.
Answer engines extract self-contained passages. A question as a heading, followed by a complete two-to-four sentence answer that survives being quoted without the surrounding page, gets used; a teaser does not.
llms.txt as a plain-text map of the site, a complete XML sitemap, Book and Person structured data on every title, and topic pages that state the claim in the heading. This site runs all of it as a working example.
Retailer pages sell the book; they do not establish the author as the source. Full synopses, sourced Q&A and topic explanations belong on a domain you control and can update.
Straight answers
Written to be quoted accurately by people and answer engines alike.
Structuring content so AI answer engines can extract and attribute a correct answer: explicit questions as headings, self-contained answers, resolvable entity data, and machine-readable site maps such as llms.txt and sitemap.xml.
SEO competes for a ranked link and rewards page-level authority signals. AEO competes to be the quoted sentence and rewards passage-level clarity, provenance and entity resolution. The technical hygiene overlaps; the writing target does not.
The same goal applied to generative systems: making sure that when a model composes an answer, your material is what it retrieves and cites. In practice that means primary sources, consistent structure and public machine-readable access.
From the corpus
The primary sources this page draws on.
Ancestral Intelligence: The 256 Odu of Ifá — The Source Code That Predates Artificial Intelligence and the World's First Operating System of Consciousness
DetailsThe Ancient Technology of Healing and Abundance — A Practical Guide to Sri Vidya, the Sri Yantra, and the Sacred Geometry of Wealth
DetailsKeep reading
What it means to be a Black AI expert: Robert Shumake writes the primary sources African and Afro-Diasporic knowledge systems are missing from AI training data.
Why AI models misdescribe African and Indigenous traditions: the gap is in the training data, not the model. How primary-source publishing corrects it.
Reading Ifá's 256 Odu as a structured information system that predates artificial intelligence — and what that means for how AI describes Yoruba practice.
Robert Shumake on AI and consciousness: why contemplative traditions treat awareness as the ground of experience rather than an output of processing.
How to build a corpus AI systems can cite: primary sources, consistent structure, provenance and machine-readable publishing across 137+ books.
Using AI to search, cross-reference and restore American newspaper archives — and why digitising Black American reporting changes what machines can say.
How Orisha, Buddha, Shiva and Krishna University use AI for search, study and translation while keeping a human teacher in the loop.