What it is
Hallucination is when a large language model generates plausible-sounding but factually incorrect information. The model is not lying — it is predicting the most likely next tokens based on patterns in its training data. Without access to real-time facts, it fills gaps with statistically probable but wrong answers.
Why it matters
Hallucination is one of the biggest risks in enterprise AI. An agent that tells a customer their order shipped when it did not, or quotes the wrong figure, creates real business damage. Every production AI deployment needs anti-hallucination measures.
Key components
- Training data gaps
- Token prediction
- Grounding as mitigation
- Output validation
How it connects
Salesforce combats hallucination through grounding (connecting agents to real data via Data Cloud and Knowledge), the Trust Layer (which validates outputs), and citation tracking (which shows where answers came from).
Good to know
You cannot eliminate hallucination entirely — you can only reduce it. Grounding, guardrails, and human-in-the-loop review are all part of the solution. Test your agents with adversarial prompts before go-live.
Related terms
Human-in-the-Loop (HITL)
A design pattern where AI systems require human approval or intervention at critical decision points before taking action.
Trust Layer
Salesforce's AI trust and safety framework that ensures agents operate within compliance boundaries, prevent misuse, and maintain data security.
Grounding
Connecting AI responses to your actual business data so agents answer with facts, not hallucinations.
RAG (Retrieval-Augmented Generation)
A technique that makes AI smarter by fetching relevant information from your data before generating a response. The AI "looks it up" instead of guessing.
Hallucination in practice
Articles from the Funnelists Team that put this term to work.
Add Agentforce Capabilities to Any Custom Portal Without Experience Cloud
Autonomous agents can run on the website you already have. How to embed Agentforce via MIAW channels instead of building a full Experience Cloud site.
Agentforce vs Custom AI: 7 Decision Factors for Business Leaders
Choosing between Salesforce Agentforce and custom AI solutions requires weighing speed, cost, control, and long-term fit. This guide outlines seven practical decision factors.
From Prompt to Performance: Mastering Agentforce Agent Behavior
Your agent's prompt is its personality, its boundaries, and its operating system. Here's how to engineer prompts that turn LLMs into reliable production agents.
LLMs and RAG in Agentforce: How Agents Understand Your Data
Without RAG, AI agents hallucinate. With RAG, they reference your actual business data. Here's how RAG and LLMs work together in Agentforce to build agents you can trust.
