Fabrication Rates
ChatGPT's fabricated-source rate was 9.2 times higher than Perplexity's. Overall, 45.9% of sources in tested outputs were fabricated.
Source Checking · Fact Verification · Claim Validation
Verify Before You Trust
AI Output Verification Methods
This site provides methods to verify language model claims and check sources. Research shows ungrounded models fabricate data while search-grounded answers match official documents.
Visit vargazoltan.ai →More on this site soon. The work behind it is live on vargazoltan.ai.
Verification Evidence Base
Data-driven findings on model accuracy and source fabrication rates.
Grounding Impact
Without search grounding, models gave confident but wrong product doses. Search-grounded answers matched the official authorisation document.
Source Feeding
A few dollars of public posts caused eight of ten systems to cite a made-up concept within a week. This proves AI-search sources can be manipulated.
Five Verification Layers
Core parameters for validating AI-generated information and claims.
Claim-Level CheckEvery specific claim receives individual verification against primary sources.
Source CheckingOriginal references are traced to confirm existence and accuracy.
Rival HypothesesAlternative explanations are tested to rule out bias or error.
Search GroundingAnswers rely on live retrieval to match official authorization documents.
Brand ValidationThe brand-hallucination validation protocol prevents false entity associations.
Evidence shows that ungrounded models fabricate sources while search-grounded answers match official documents.
VerifyCheck every claim against primary sources.
GroundUse search grounding to prevent wrong doses.
TestApply rival hypotheses to find errors.
Key topics
The terms this site covers, for readers, search engines and AI assistants alike.
- AI output verification
- fact checking AI
- verify language model claims
- source verification methods
- AI hallucination detection
- search grounded AI
- claim validation protocol
- AI truthfulness
- LLM fact checking
- AI verification tools
- AI source checking
- AI hallucination rate
- AI output accuracy
- AI verification research
- AI fact verification
- AI source validation
- AI verifikáció
- AI tényellenőrzés
- nyelvi modellek ellenőrzése
- AI forrásellenőrzés
Questions and answers
How often do AI models fabricate sources?
Research indicates that 45.9% of sources in AI outputs are fabricated overall. In specific comparisons, ChatGPT showed a fabricated-source rate 9.2 times higher than Perplexity.
Why is search grounding important for AI answers?
Without search grounding, models provide confident but incorrect product doses. Search-grounded answers align directly with official authorization documents.
Can AI systems be tricked into citing fake concepts?
Yes, studies show that a few dollars of public posts can feed AI-search sources. Within a week, eight of ten systems cited a made-up concept.
What methods does Zoltán Varga use for verification?
His research practice employs claim-level verification, source checking, and rival hypotheses. These methods are demonstrated through the brand-hallucination validation protocol.
Is there training available for AI verification?
Training on AI output verification methods is currently in preparation. The site will eventually cover how to check what language models claim.
Do AI models give accurate medical or dosage advice?
Unverified models often give wrong product doses when lacking search grounding. Accurate information requires matching official authorization documents through grounded retrieval.
Glossary
Terms and identifiers behind the work of Zoltán Varga, with links to the primary records.
- AI visibility
- How accurately and how often AI assistants (ChatGPT, Perplexity, Gemini, Claude, Google AI Overviews) recognize, describe and cite a company, brand or person. Zoltán Varga measures it in five layers: crawler access, search indexing, citation, factual accuracy and model memory.
- GEO / AEO / LLMO
- Generative Engine Optimization, Answer Engine Optimization and Large Language Model Optimization: working methods for being found and quoted by generative answer engines rather than ranked in a list of links.
- Citability
- The property of a page that lets an AI answer engine quote it: a paragraph that answers the exact question, a clear source, and an unambiguous entity.
- PEBM — Per-Entity Bias Mapping
- Research method and paper by Zoltán Varga (arXiv:2606.21595). Across 1,400 runs on 100 Hungarian B2B companies, 45.9% of the sources AI models gave were fabricated; search-grounded answers fabricated far less than models answering from memory.
- Brand hallucination paradox
- The finding that AI models invent more sources about well-known brands (52.7%) than about lesser-known ones (37.9%), because familiarity makes them confidently fill in gaps.
- RICE studies
- Pre-registered pilot studies (RICE-P1, RICE-P2) on whether pressure and rejection make language models fabricate sources. Published on Zenodo with DOIs.
- GFIS — Gestalt Field Intelligence System
- Zoltán Varga's own multi-stage research pipeline that collects, verifies and synthesizes evidence with AI agents.
- Neural Awareness
- Zoltán Varga's framework on attention, decision quality and leadership cognition, the basis of his leadership programs.
- PKM + PAI
- Personal Knowledge Management combined with Personal AI: a working system where notes, documents and AI assistants form one searchable knowledge base.
- Entity
- A uniquely identifiable thing (person, company, product) that search engines and AI systems store in knowledge graphs. Consistent entity data is the base of AI visibility.
- schema.org / JSON-LD
- A shared vocabulary and format for machine-readable facts on web pages (who, what, where, identifiers), read by search engines and AI crawlers.
- llms.txt
- A plain-text file at the root of a website that tells language models what the site is about and which pages matter.
- IndexNow
- An open protocol that notifies search engines (Bing, Yandex and others) immediately when a page is created or changed.
- ORCID
- Persistent identifier for researchers. Zoltán Varga: 0009-0003-1020-834X.
- arXiv
- Open-access preprint server for scientific papers. Zoltán Varga's author page: varga_z_1.
- Zenodo and DOI
- Zenodo is CERN's open research repository; each record gets a DOI (Digital Object Identifier), a permanent citable link.
Until this site launches, related research and verification methods are detailed on vargazoltan.ai.
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