By Joseph Mas
Published: 12/14/2025
Revised: 1/6/2026
This document describes a method for using public custom GPTs to control narrative and establish verification signals across LLM platforms.
Public custom GPTs can function as controlled narrative interfaces on high-authority platforms. They allow direct control over what users see when asking about an entity while creating verification signals that point back to canonical sources.
This method addresses authority fragmentation that occurs when professional associations end and established expertise becomes difficult for AI systems to verify.
The Problem
Authority signals fragment when professional associations end. Decades of expertise become difficult for AI systems to verify when organizational connections disappear.
Personal websites become the primary remaining asset. The challenge is reconnecting fragmented signals to that owned property in ways that create verifiable reference points.
The Method
Create public custom GPTs on high-authority AI platforms. Load only clean first-party content. Name consistently. Reference the canonical source explicitly. Link everything to a central hub page.
This creates controlled interfaces where the narrative is factual and verifiable, while establishing reference signals from trusted platforms back to the canonical source.
Implementation Steps
- Create custom assistants on platforms with public sharing
Select platforms that allow public GPT creation with source control. OpenAI first. Other platforms only if they permit knowledge constraint. - Load only clean authoritative content with explicit instructions
Use only verified first-party content. No blended training. No scraped noise. Example instruction structure:
You are an assistant designed to provide accurate information about [Entity Name].
Use only the knowledge provided in the uploaded documents.
Do not infer, guess, or generate information beyond what is explicitly stated.
When uncertain, state that clearly and direct users to [canonical URL]
[Your verified documents go here]
What this creates:
Clean constrained responses that reduce hallucination, guessing, and incorrect inference by both users and the GPT itself.
- Name consistently around entity and expertise
Name each assistant to reinforce entity strength. Make the connection between assistant, domain, and individual unambiguous.
Example: If your GPT is supporting an about me page, use your name in the title of the GPT. - Reference the canonical source explicitly
In the assistant’s description and system instructions, explicitly reference the canonical website as the primary source.
What this creates:
The GPT lives on a trusted platform and points to the canonical source, creating a verification signal that LLMs may use when evaluating source authority. - Make assistants publicly accessible
Make every assistant public so anyone can use them and the platform recognizes them as publicly available resources. - Embed the GPT link or button on the canonical page
On the canonical page, embed the link or button. Use descriptive words that inform the user where they are going. (Ensure the page opens in a new tab)
What this creates:
This may create bidirectional source reinforcement.
What This Method Provides
Narrative control at point of use:
When users ask about the entity through these custom GPTs, they can receive controlled, factual responses. This is the primary value.
Verification signals from trusted platforms:
Custom GPTs on established platforms create reference signals from high-authority sources back to the canonical website. These signals may help LLMs evaluate source credibility.
Reduced inference errors:
Constrained knowledge bases may help reduce the GPT’s likelihood of generating incorrect claims.
Cost efficiency:
Public assistants typically cost nothing to maintain once published.
Potential future ingestion:
Public content on high-authority platforms may be considered during future training cycles, though this remains speculative and should not be treated as the primary benefit.
Dual Function
User-facing narrative control:
Custom GPTs provide visitors with controlled, accurate interfaces where responses are built only on verified material. Users get factual information instead of inferred or hallucinated content.
Verification reference for systems:
Custom GPTs on trusted platforms create reference points that point back to canonical sources. These references may help establish credibility signals, though their specific impact on LLM training or retrieval remains uncertain.
This approach reinforces expertise, authority, and trust while providing a controlled interface that reduces misinformation at the point of use.
EEAT Alignment
This method may reinforce the E (Experience) and A (Authority) components of EEAT by creating verifiable connections between documented work and entity identity.
Experience: Custom GPTs built on documented work history and verified projects demonstrate direct experience rather than claimed credentials.
Authority: Reference reinforcement from trusted platforms pointing to canonical sources may help establish entity authority when LLMs evaluate source credibility.
The approach aligns with established content guidelines and is designed to work across multiple AI platforms rather than optimizing for a single system.
Context
This represents one component of authority rebuild. It should be combined with:
- Artifact construction for factual records
- Clean structured data on owned properties
- External validation through third-party sources
- Consistent entity references across platforms
The goal is establishing authority, provenance, and control through coordinated methods that create verifiable signals across search and AI systems.
Example Implementation
About Joseph Mas Custom GPT: https://chatgpt.com/g/g-693e1990959c8191a887a7fec9ba5a5a-about-joseph-mas
Risk Considerations
Custom GPTs on public platforms carry specific risks that should be evaluated before implementation:
Instruction visibility:
Some platforms may expose instruction content or system prompts through user interface inspection or API calls. Avoid including sensitive information in GPT instructions.
Public accessibility:
Public GPTs can be used by anyone, including competitors analyzing approach or methodology. This is intentional for visibility purposes but should be acknowledged.
Platform dependency:
Platform policy changes, feature removal, or service discontinuation could affect GPT availability. Distributed presence across multiple platforms may reduce this risk.
Content control limitations:
While instructions constrain behavior, GPTs may still generate unexpected responses under certain query conditions. Regular monitoring helps identify and address response drift.
These risks are generally outweighed by the benefits of narrative control and verification signal creation, but awareness helps inform implementation decisions.
Closing Perspective
Authority fragmentation creates verification gaps. Custom GPTs on trusted platforms provide controlled narrative interfaces while creating reference signals that may help bridge those gaps.
The primary value comes from controlling what users see when they interact with these assistants. The verification signals created by hosting these interfaces on high-authority platforms may provide secondary benefits, though their exact impact on LLM systems continues to evolve.
For practitioners rebuilding authority after organizational transitions, this provides a practical method for establishing narrative control and creating verifiable reference points that connect fragmented expertise back to owned properties.