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Hallucinations are the Achilles' heel of AI language models. The model generates convincingly-sounding information that is factually incorrect, without any awareness that it's wrong. For B2B agents communicating on behalf of your organization, this is a serious risk.
Why Do AI Models Hallucinate?
Language models predict the most likely next word based on statistical pattern recognition. They don't 'know' what is true or false, they generate plausible text. When the model is uncertain but generates an answer anyway instead of saying 'I don't know,' hallucination occurs.
The Five Most Effective Techniques to Reduce Hallucinations
- 1. RAG: the agent always consults a verifiable knowledge source before answering, answers are traceable to concrete documents
- 2. Strict system prompts: explicitly instruct the agent to say 'I don't know' when uncertain, and never guess on factual claims
- 3. Lower temperature: a lower temperature setting makes the model more conservative, less chance of fabricated facts
- 4. Grounding checks: have a second AI step verify the output against sources before it reaches the user
- 5. Human-in-the-loop for critical output: have a human review output before impactful communications are sent
Conclusion
Hallucinations are inherent to how language models work, completely eliminating them is impossible. But they can be managed to an acceptable level through the right architectural choices (RAG), prompt strategy, and monitoring.
