Hallucination (AI)

Why hallucinations happen

LLMs are predictive text generators. When they have strong training signal about a topic, they generate accurate text. When they have weak or contradictory signal, they fill the gap with the most statistically-plausible continuation — which may be wrong.

Common triggers:

• Niche topics with sparse training data
• Brand or product details that changed recently (post-training-cutoff)
• Conflicting information across multiple sources
• Underspecified prompts where the model has to guess

Why hallucinations matter for brands

A user asking ChatGPT “what does [your brand] do?” gets an answer that may or may not be accurate. If the model hallucinates:

• Wrong founding year → undermines credibility
• Wrong service offering → misleads buyers about fit
• Invented product features → support tickets when reality doesn’t match
• Misattributed quotes or studies → reputational risk

Most brands don’t audit this. Most are being hallucinated about today and don’t know it.

How to reduce hallucinations about your brand

Two layers of defense:

Layer 1: Strong factual signal

• Comprehensive Organization schema with accurate properties
• Wikidata Q-entity with verified statements (companies, founders, dates)
• Wikipedia entry where eligible (highest-leverage single signal)
• Consistent NAP, founding date, leadership across web mentions

When ChatGPT has 10 corroborating sources for “Resocial was founded in 2023,” it hallucinates that fact much less often.

Layer 2: Monitoring + correction

• Citation tracking across 5 AI engines for branded queries
• Quarterly hallucination audit (run 20 branded queries, log inaccuracies)
• Wikipedia / Wikidata edits for ground-truth corrections
• Schema updates to surface authoritative facts

You cannot directly correct an LLM’s output, but you CAN shift the ground-truth corpus it retrieves from.

RAG reduces hallucinations — partly

LLMs with RAG (browsing-mode ChatGPT, Perplexity) hallucinate LESS because they retrieve current content and cite it. But they hallucinate the SUMMARIES of retrieved content — meaning they may cite the right source but misrepresent what the source says.

Resocial perspective

We treat hallucination monitoring as part of every AI Search engagement. The quarterly audit catches drift; the schema + entity authority work prevents it from happening in the first place. Most brands learn about hallucinations from customer support tickets (“ChatGPT said you offer X — but you don’t”). Better to learn from monitoring.