Articles
Guides and analysis on proof42.ai
Twelve in-depth articles by Zoltán Varga. Each one answers a single question, starting with the short answer.
Writing an AI Output Verification Policy for Your Organization
A practical framework for sorting AI claims into citable, hypothetical, or discarded categories with clear ownership and verification steps.
Why RAG Systems Cite the Wrong Source When Retrieval Works
Retrieval succeeds, but the synthesis layer misassigns credit to the wrong documents or invents sources entirely.
RAG Attribution Audit: A Checklist for Answer Reviewers
A practical guide for reviewers to validate every attribution in retrieval-augmented generation outputs.
Building a Claim-Level Verification Pipeline for AI Reports
Shift from document-level review to atomic claim verification for auditable, high-trust AI outputs.
Evidence Architecture: Key Terms for Verifying AI Claims
A precise glossary defining the structural elements required to turn AI output into a legally and technically defensible record.
How to Build a Test Question Set for AI Answer Accuracy
A fixed, tagged question set with a scoring rubric reveals empty results and freshness gaps before users encounter them.
Question Deconstruction and Blind-Spot Audits for AI Research
A structured approach to prevent confident errors by breaking down questions and auditing missing information.
Does Multi-Model Adversarial Review Catch More AI Errors?
Independent model voting outperforms adversarial debate for catching AI errors, provided sessions remain isolated and prompts enforce constructive critique.
Pre-Registering AI Output Tests So the Results Hold Up
A pre-registered, cross-platform study reveals why post-hoc testing fails and how protocol-first design builds audit-ready AI verification.
Auditing What AI Systems Say About Your Organization
A high visibility score often masks confident falsehoods about your brand; this guide explains how to audit, classify, and correct AI-generated claims.
Corrective RAG: Making Retrieval Pipelines Check Themselves
A practical framework for embedding verification and self-correction into retrieval-augmented generation pipelines.
AI Verification Governance: Who Signs Off When AI Gets It Wrong?
Accountability for AI-generated content rests on the human who authorizes the final output, not the algorithm that created it.