# proof42.ai

> Proof42 offers a methodology for verifying AI output. Learn how to check language model claims using evidence-based source and fact verification.

This site details a methodology for verifying AI output and checking language model claims. Research shows models fabricate sources and provide confident but incorrect information without search grounding.

## Author

- Zoltán Varga — AI visibility researcher and AI ecosystem architect, Budapest: https://vargazoltan.ai/
- English profile: https://zoltanvarga.ai/en/about/
- ORCID: https://orcid.org/0009-0003-1020-834X
- arXiv author: https://arxiv.org/a/varga_z_1.html
- Zenodo: https://zenodo.org/search?q=owners%3A1653886

## Publications

- Per-Entity Bias Mapping for AI Visibility: Why Brand Mentions Require Entity-Specific Calibration (arXiv:2606.21595): https://arxiv.org/abs/2606.21595
- AI Visibility in the Generative Era: A Manifesto for Entity-Level Measurement (DOI 10.5281/zenodo.20285424): https://doi.org/10.5281/zenodo.20285424
- Quality Challenges Do Not Escalate Source Fabrication in Large Language Models (RICE-P1) (DOI 10.5281/zenodo.20474050): https://doi.org/10.5281/zenodo.20474050
- Rejection-Induced Confabulation Escalation — Pilot Study 2 (RICE-P2) (DOI 10.5281/zenodo.20509755): https://doi.org/10.5281/zenodo.20509755

## Key topics

- AI verification methodology
- check AI output
- language model fact checking
- source verification AI
- AI hallucination detection
- verify AI claims
- AI trust framework
- fabricated sources AI
- search grounded answers
- AI safety research
- AI visibility researcher
- Zoltán Varga AI
- AI source checking Hungary
- ellenőrzés mesterséges intelligencia
- AI tényellenőrzés
- forrás hitelesítés AI
- AI verifikáció módszertan
- hallucináció detektálás

## Questions and answers

### How do I know if an AI citation is real?

Check the original source document directly rather than trusting the AI summary. Research shows that without search grounding, models frequently fabricate sources at high rates compared to search-grounded tools.

### Why do AI models give wrong product doses?

Models generate confident but incorrect information when they lack search grounding. When connected to official authorization documents via search, the answers match the correct data.

### Can AI systems be tricked into citing fake concepts?

Yes, a small number of public posts can cause multiple AI systems to cite a made-up concept within a week. This proves that AI-search sources can be fed false information easily.

### What is the difference between ChatGPT and Perplexity regarding sources?

Studies show ChatGPT's fabricated-source rate was significantly higher than Perplexity's. On human-coded endpoints, Perplexity achieved 100% accuracy while ChatGPT scored 15.7%.

### Does this site offer verification services?

No, this site describes a verification methodology and presents research findings. It does not offer commercial verification services under the Proof42 name at this time.

### Who developed the verification protocol mentioned here?

The protocol uses practices developed by Zoltán Varga, including claim-level verification and source checking. These methods are part of his research on neural knowledge systems.

## Related

- Research: https://vargazoltan.ai/kutatas/
- About: https://vargazoltan.ai/en/about/
- Contact: https://vargazoltan.ai/kapcsolat/

## Articles

- [Writing an AI Output Verification Policy for Your Organization](https://proof42.ai/articles/ai-output-verification-policy/): An organization should classify every AI-generated claim into three tiers—citable, hypothesis, or discard—and tie each tier to a specific verification method and a named owner. This policy replaces ad-hoc checks with a repeatable pipeline that logs model versions, confidence scores, and provenance for every output before it enters production.
- [Why RAG Systems Cite the Wrong Source When Retrieval Works](https://proof42.ai/articles/why-rag-cites-the-wrong-source/): A RAG system attributes claims to the wrong document because the error occurs during synthesis, not retrieval. Even when the correct passages are found, the model confuses secondary summaries with primary sources, mistakes topical neighbors for the origin, or generates citations for non-existent works.
- [RAG Attribution Audit: A Checklist for Answer Reviewers](https://proof42.ai/articles/rag-attribution-audit-checklist/): To audit citations in a RAG-generated answer, extract every attributed claim and run a confirming search for each author and title. Match each claim to the exact supporting passage rather than the whole document, then record the verdict and specific evidence.
- [Building a Claim-Level Verification Pipeline for AI Reports](https://proof42.ai/articles/claim-level-verification-pipeline/): Build a claim-level verification pipeline by detecting check-worthy assertions, splitting them into atomic statements, linking each to specific evidence sources, and assigning a verdict. This process isolates errors for targeted correction rather than regenerating entire documents. It replaces broad document review with granular, auditable fact-checking at the statement level.
- [Evidence Architecture: Key Terms for Verifying AI Claims](https://proof42.ai/articles/evidence-architecture-glossary/): Evidence architecture defines the structural links between a claim and its source, requiring a verifiable origin chain rather than a simple citation. Terms like rival hypothesis and blind-spot audit describe the mandatory inclusion of counter-arguments and untested inference paths to prevent confirmation bias. These concepts transform AI output from unverified text into an auditable process where every assertion carries a specific evidentiary status.
- [How to Build a Test Question Set for AI Answer Accuracy](https://proof42.ai/articles/golden-question-set-ai-accuracy/): Organizations measure AI accuracy by running a fixed set of 70 domain-tagged questions against the system and scoring key concept hits. This process exposes empty results, freshness gaps, and weak domains that user impressions miss. The baseline data shows that without a reranker, concept hit rates can drop below 9% even with millions of chunks.
- [Question Deconstruction and Blind-Spot Audits for AI Research](https://proof42.ai/articles/question-deconstruction-blind-spot-audit/): You stop an AI research pipeline from confidently answering the wrong question by deconstructing the inquiry into its core elements before execution and auditing the output for missing perspectives afterward. This two-phase method forces human oversight at the start and end of the process, ensuring the system addresses the actual decision need rather than just matching keywords.
- [Does Multi-Model Adversarial Review Catch More AI Errors?](https://proof42.ai/articles/multi-model-adversarial-review/): Multi-model adversarial review catches more errors primarily through independent voting rather than through debate itself. The gain comes from aggregating diverse outputs to identify blind spots, not from models arguing back and forth. This approach requires strict session isolation to prevent state collision and constructive prompt engineering to avoid destructive feedback loops.
- [Pre-Registering AI Output Tests So the Results Hold Up](https://proof42.ai/articles/pre-registered-ai-output-testing/): Design trustworthy AI output tests by writing the protocol and defining success metrics before running a single query, then documenting every deviation openly. Use factorial designs across platforms and conversation depths to isolate variables, and report results even when the initial hypothesis is disproven. This approach transforms subjective quality checks into auditable, repeatable evidence.
- [Auditing What AI Systems Say About Your Organization](https://proof42.ai/articles/brand-hallucination-audit/): Companies must move beyond aggregate visibility scores and implement entity-level audits that measure the factual accuracy of every claim AI systems generate. You need to distinguish between parametric memory errors, which take 12 to 24 months to resolve, and retrieval-based errors, which can be corrected in weeks. The goal is to identify and fix confident false attributions before they become entrenched in the model's output.
- [Corrective RAG: Making Retrieval Pipelines Check Themselves](https://proof42.ai/articles/corrective-rag-explained/): Corrective RAG pipelines detect weak evidence by evaluating retrieved documents for relevance before generating a final answer. When the system finds insufficient or conflicting data, it triggers conditional actions like re-searching the web or selectively regenerating only the flawed passages. This process prevents hallucinations by ensuring every factual claim is supported by verified context.
- [AI Verification Governance: Who Signs Off When AI Gets It Wrong?](https://proof42.ai/articles/ai-verification-governance-faq/): The individual or role with final authorization authority for the document is accountable when AI-generated content proves incorrect. This requires a documented audit trail linking the specific model version, input data, and human approval step to the released content. Responsibility shifts from a communication issue to an engineering requirement once AI enters production systems.