Verification Protocol for Domain-Source Frequency Claims in AI Assistants
Verification Protocol for Domain-Source Frequency Claims in AI Assistants
AIVO Standard Data Note — Domain Attribution Methodology v1.1
Introduction
Claims about which domains AI assistants rely on have begun circulating widely across marketing, communications, and analytics circles. These claims are increasingly used to inform brand strategy, misinformation assessments, and executive decisions about AI visibility. Yet most are built on opaque prompt samples, unclear classification rules, and methods that cannot be independently reproduced.
This creates a foundational governance risk. Incorrect assumptions about domain influence can distort how organisations perceive their exposure to misinformation, how their brands surface in AI mediated environments, and how much confidence they can place in third party dashboards. Without reproducibility and full methodological disclosure, domain-source claims cannot serve as reliable evidence in any enterprise, audit, or regulatory context.
The protocol below sets the minimum evidentiary standard required for domain-source frequency claims to be considered valid under the AIVO Standard.
1. Purpose
This document defines the mandatory methodological requirements for producing, disclosing, and independently verifying domain-source frequency claims in AI-assistant outputs.
Domain-source claims currently circulating in industry analysis illustrate the need for a reproducible, audit-grade verification protocol. This methodology establishes the minimum evidentiary criteria required for such claims to be considered valid within enterprise visibility assessment, governance reporting, and third-party decision support.
2. Scope
This protocol applies to domain-source frequency evaluations involving:
- ChatGPT
- Claude
- Gemini
- Perplexity
It shall apply to both single-prompt and multi-prompt journeys.
Any study that publishes domain-source frequency results and seeks recognition under the AIVO Standard shall comply with Sections 3–8 of this protocol.
3. Prompt-Set Disclosure Requirements
3.1. The full prompt set shall be published without omission.
3.2. Each prompt shall be classified by:
- query type (informational, transactional, comparative, troubleshooting, brand, non-brand)
- vertical domain
- intended user task
3.3. A minimum of 300 prompts shall be used unless a higher number is tested and justified. - 3.4. Studies that fail to publish the complete prompt set shall be classified as Non-Verifiable (see Section 7).
4. Assistant-Level Weighting Requirements
4.1. When aggregating results across assistants, domain frequencies shall be weighted by estimated real-world usage, not sample count.
4.2. Weighting assumptions, including data sources and estimation logic, shall be published.
4.3. Weighted and unweighted results shall be provided side-by-side.
4.4. Omission of weighting disclosure shall result in a Methodologically Deficient classification.
5. Source-Attribution Classification Requirements
5.1. The study shall publish explicit rules governing domain-source classification.
5.2. The classification system shall distinguish between:
- explicit citations
- implicit citations
- paraphrased or stylistic references
- hallucinated or synthetic references
5.3. UGC-derived conversational patterns shall not be classified as domain sources without explicit evidence of derivation.
5.4. Failure to differentiate tone from origin shall invalidate domain-ranking claims and result in a Classification Error.
6. Replay and Reproducibility Requirements
6.1. All results shall be reproducible via a disclosed replay protocol.
6.2. Replays shall include:
- timestamp
- model identifier/version
- complete multi-step journey
- assistant settings
- capture and logging protocol
6.3. A result shall be considered reproducible if independent replay yields domain frequencies within a ±5 percent tolerance band.
6.4. Variance beyond tolerance shall result in a Non-Reproducible classification, unless the deviation can be causally attributed to model updates.
7. Classification Outcomes
Studies evaluated under this protocol shall be classified as:
- Compliant
Meets all requirements in Sections 3–6 and is reproducible within tolerance. - Non-Reproducible
Replay results fall outside tolerance and cannot be attributed to model updates. - Methodologically Deficient
One or more mandatory requirements in Sections 3–5 are not met. - Non-Verifiable
Insufficient methodological disclosure to allow independent replay.
No comparative or performance-based statements shall be issued by AIVO.
8. Governance Rationale
Unverified domain-source claims create enterprise visibility risk by:
- misrepresenting the origins of assistant-generated content
- distorting executive understanding of assistant behavior
- undermining the reliability of dashboards, reports, and analytics
- preventing audit and regulatory teams from applying consistent evaluation standards
This protocol establishes the minimum methodological standard required for domain-source claims to be considered evidence within governance processes.
9. Implementation
AIVO shall apply this protocol to recurring verification sweeps across the major AI assistants listed in Section 2.
Organisations may submit methodologies for evaluation. Compliance shall be determined solely based on adherence to the requirements outlined above.
10. Validity Conditions
10.1. Only studies classified as Compliant under Section 7 shall be considered valid evidence for enterprise visibility analysis, governance reporting, regulatory submissions, or strategic decision support.
10.2. Studies classified as Non-Reproducible, Methodologically Deficient, or Non-Verifiable shall not be used as inputs to enterprise visibility frameworks or decision-making processes.
10.3. Any use of non-compliant studies in governance contexts shall constitute a visibility-assurance failure.
Applied Example — Reddit: Source Classes in Practice
When assessing domain frequency claims, it is essential to distinguish between authoritative sources and anecdotal ones. Observable retrieval behavior in modern AI assistants shows that Reddit functions primarily as a qualitative, user-sentiment source rather than an authoritative one. Under neutral queries, assistants typically prioritise official documentation, manufacturer information, and professional reviews for factual grounding.
Reddit can surface as useful context for user experience or emergent issues, but it does not carry the evidentiary weight associated with official or professional sources. Under the AIVO verification protocol, claims about Reddit’s influence must therefore be tested across source classes and validated through controlled, clean-session replay, not inferred from raw mention volumes or stylistic cues.
Conclusion
The issue isn’t that a single prompt disproves Reddit’s usefulness. It is that repeated clean-session tests expose a mismatch between the assertion that Reddit holds a asserted role in AI retrieval and the patterns visible in assistant outputs.
When personalization is stripped and neutral product or service prompts are used, the assistant’s answers consistently rely on official documentation, manufacturer data, and established professional reviews. Reddit appears mainly as anecdotal or experiential context.
This raises a broader methodological concern. If a headline claim about Reddit’s influence collapses under straightforward reproducibility checks, the underlying measurement approach is not robust. Reliance on isolated snapshots, unverified source assumptions, and opaque sampling frameworks makes such outputs unsuitable for governance, audit, or enterprise decision contexts.
The story here is not intent. It is a verification failure that demonstrates why claims about “dominant sources” in AI assistants require cross-model, cross-prompt, clean-session validation. Without an evidence chain, dashboards can create the appearance of influence that is not reflected in actual assistant behavior.
This is the precise gap the AIVO Standard is designed to close.