GPTfy Glossary
Hallucination (AI)
When an LLM produces output that sounds plausible but is factually wrong or fabricated — e.g. citing a non-existent record or inventing a policy detail.
Hallucination is the #1 enterprise blocker for LLM deployment. The model's job is to produce fluent text — and when it doesn't know the answer, it produces fluent text *anyway*. This is dangerous in CRM contexts: a sales rep could read a confidently fabricated customer history and act on it.
Mitigations include: (1) grounding the model with retrieved facts via RAG; (2) prompting the model to say "I don't know" when uncertain; (3) using model "temperature" settings near zero for deterministic outputs; (4) verification layers that fact-check generated content against source records; (5) human-in-the-loop review for high-stakes outputs.
For Salesforce, anti-hallucination is implemented at multiple layers: Einstein Trust Layer's grounding, gptfy's Security Layer's source-citation requirement, and architecture patterns where the LLM can only return content that's traceable to a source record.
See Hallucination (AI) in GPTfy
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