AI Hallucination
An AI hallucination occurs when an LLM confidently generates incorrect, fabricated, or non-existent facts not grounded in real source data.
An AI hallucination is a phenomenon where a large language model generates factually incorrect or fabricated statements presented with high linguistic confidence, unsupported by real-world context.
How Hallucination works in practice
LLMs are probabilistic token predictors, not factual databases. When an ungrounded model encounters an unfamiliar question, it predicts the most statistically probable next words, frequently inventing non-existent refund policies, discounts, or API endpoints.
The only deterministic defense against hallucinations is strict RAG: threshold gating that prevents the model from answering when authoritative context is missing.
How SiteMind implements Hallucination
SiteMind is engineered with a zero-hallucination guarantee. Answers are synthesized exclusively from your verified website chunks, and missing information triggers an honest refusal.
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Related Technical Concepts
Source Grounding & Citations
Source grounding ensures every AI claim is directly supported by retrieved website text and includes clickable citations for instant human verification.
Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) is a technique where an AI fetches verified passages from a knowledge base before answering, ensuring grounded, factual replies.
Cosine Similarity Thresholds
Cosine similarity measures the angle between two embedding vectors in multidimensional space to determine how closely their meanings match.
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