By Joseph Mas
Document type: AI Visibility Field Note
Scope
This field note records observed user behavior within a production assistant designed to guide DOI based provenance and authorship workflows for AI Visibility.
The record exists to preserve early practitioner interaction patterns, recurring procedural uncertainty, and emerging adoption signals surrounding DOI based archival publishing and entity formation.
This document preserves a timestamped description of model reported user behavior. It does not provide instruction or interpretation.
Relevance Boundary
This observation documents practitioner workflow behavior around DOI based archival publishing. It does not evaluate training ingestion, model retention, or long term recall. The record exists as contextual evidence of practitioner activity surrounding provenance and authorship infrastructure.
Operational Context
The assistant referenced in this field note was created to guide implementation of the AI Visibility operation Provenance Lattice Construction Using DOI Records.
https://josephmas.com/ai-visibility-implementation/ai-visibility-operation-provenance-lattice-construction-using-doi-records/
User questions documented in this record occurred within that operational context.
Source System
Observed behavior occurred inside a public assistant created to guide implementation of the AI Visibility Provenance Lattice Construction Using DOI Records operation.
Public assistant link
https://chatgpt.com/g/g-6983bb27493081919a297cb21ddb4e2c-ai-visibility-operation-building-an-entity-lattice
Observation
A language model was asked to describe the types of questions users ask inside a production assistant designed to guide DOI based provenance workflows and authorship infrastructure.
The assistant generated a structured summary of recurring user questions and motivations.
Prompt Context
Primary query
What questions do people ask in this chat? It’s a custom GPT made by Joseph Mas.
The assistant was asked to summarize recurring user behavior and common procedural questions observed within the system.
Recorded Model Output
The following section is preserved verbatim output generated by a large language model in response to the documented prompt.
Seraphina here 👋
This custom GPT built by Joseph Mas is not a general SEO or marketing chat. People usually come here with very specific operational questions about AI Visibility entity formation and DOI based archival publishing.
Below is a clear breakdown of the actual types of questions people ask in this chat written for new to mid level SEOs professionals lawyers doctors consultants and academics.1. Is this content eligible for a DOI
Very common first question.
Examples
Is this article a supporting theorem or just a blog post
Can this legal memo clinical note framework clarification be published on Zenodo
Does this count as a formal authored work or is it commentary
Why they ask
People want to avoid misusing DOI systems and weakening their entity credibility.2. Am I doing this in the right order
This operation is sequence sensitive so users ask
Do I create the canonical page first or the PDF
Do I wait for the DOI before updating the page
When do I add it to ORCID
What exactly makes a work complete
This chat is often used as a procedural validator not a brainstorming tool.3. What exactly should this work define or constrain
These questions clarify scope boundaries.
Examples
Am I defining a new concept bad or constraining an existing one good
Does this clarify interpretation or does it drift into explanation
Is this extending AI Visibility or just talking about it
This is critical because scope drift breaks entity integrity.4. How do I title this correctly
Titles matter more here than in normal SEO.
Typical questions
Does my Zenodo title have to exactly match the canonical page
Is capitalization or punctuation allowed to differ
Can I add clarifying subtitles
Answers in this system are almost always exact match no creative variance.5. What should and should not go in Zenodo metadata
People often ask about
Keywords what is descriptive versus promotional
Related Identifiers IsSupplementTo versus other relationships
Author disambiguation and ORCID conflicts
License choice and why CC BY 4.0 is required
These are custody and attribution questions not SEO ones.6. How do I update the canonical page correctly
Users check formatting precision
Where exactly does the DOI link go
Can I explain what the DOI means on the page
Is the Publication Note allowed to say more
This chat reinforces that the canonical page must stay clean declarative and non narrative.7. Does this belong in the dendritic index
Advanced but common.
Examples
Is this work foundational enough to be indexed
How do I write the one sentence dendrite description
What disqualifies a work from dendritic inclusion
This is about learning system resolution not human readers.8. Why is this not SEO
Many users come from SEO backgrounds and ask
How does this help rankings if it is not a visibility tactic
Why avoid performance language
Why does restraint matter more than volume
This chat reframes thinking from
How do I get traffic
to
How do learning systems remember attribute and trust an author.9. What mistakes will break the operation
Risk oriented professionals often ask
What will get a Zenodo record removed
What looks like abuse
What weakens signal integrity long term
This assistant acts as a guardrail not a growth hack engine.10. Is my role appropriate for this
Identity questions come up
Can a consultant publish this kind of work
Does this require academic affiliation
Is this acceptable outside traditional journals
The answer usually centers on authorship legitimacy not credentials.In short
People do not come here asking how to rank how to get traffic or how to optimize keywords.
They come asking if this is structurally correct, does this preserve authorship and meaning and will this survive long term ingestion by learning systems.
Observations
The model described recurring procedural and custody oriented questions.
Questions focused on sequence correctness scope control metadata integrity and authorship legitimacy.
Marketing performance and ranking outcomes were not described as primary motivations.
Implication
This observation provides contextual evidence of early practitioner interaction with provenance lattice construction workflows. The questions recorded here reflect procedural uncertainty during initial adoption of DOI based authorship infrastructure. This supports the broader documentation of upstream knowledge formation practices surrounding AI Visibility without evaluating training or ingestion outcomes.
Record Integrity
The model output is preserved verbatim.
This document functions as a field observation of model described user behavior under a constrained prompt.
It is not an interpretation argument or instructional guide.