How Hallucination differs from Grounding, RAG, LLM
A model error is any wrong output. A hallucination is specifically a confident, fluent, plausible-sounding wrong output — which is what makes it dangerous: it doesn't trip a user's skepticism the way garbled output would.
How Mentionwell handles Hallucination
- Editorial critic enforces evidence-per-claim so generated articles are themselves grounded.
- Per-article Markdown mirrors give downstream engines clean source material to ground against.
- Inline citations to authoritative sources so claims are checkable.
Frequently asked questions about Hallucination
What is an AI hallucination?
An AI-generated output that's confidently stated but factually wrong — fake citations, invented statistics, non-existent quotes. The central failure mode of generative AI.
How do you reduce hallucinations?
Ground answers in retrieved sources (RAG), require citations, use a critic-loop to check claims, and run outputs through structured validators when the schema allows.
See also
Ship Hallucination-optimized articles automatically
Mentionwell handles Hallucination on every published article — alongside the other six optimization targets in this glossary — so you don't have to think about it per post. Drop a domain, approve the first headline, watch the pipeline run.