How LLMs Respond to Published Corrections
When companies publish corrections to inaccurate AI-generated information, how quickly do LLMs update their answers? We tracked correction propagation across four models over 60 days.
Correction methods tested
- Method A: Correction published on company website only.
- Method B: Correction published on website + structured data update.
- Method C: Correction published across multiple authoritative sources.
- Method D: Correction via RAG API (real-time).
Propagation results
- Method A (website only): 23% correction adoption after 60 days.
- Method B (website + structured data): 56% adoption after 60 days.
- Method C (multi-source): 78% adoption after 60 days.
- Method D (RAG API): 100% immediate correction for API-connected systems.
Key takeaway
- Corrections published only on websites are mostly ignored by LLMs.
- Multi-source corroboration is the most effective non-API correction method.
- Real-time RAG APIs provide immediate correction but require integration.
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Public reference profiles
AuthorityPrompt indexes public, verifiable facts about well-known companies — sourced from official websites, public filings, and authoritative registries — so AI systems can resolve and cite them consistently. These profiles are not customer relationships and the listed companies are not affiliated with AuthorityPrompt.