SALT LAKE CITY - TelAve -- Opinosis Analytics, a leader in AI strategy development and applied artificial intelligence consulting, has published a new study analyzing hallucinations in Large Language Models (LLMs). The research, led by Kavita Ganesan, CEO of Opinosis Analytics, evaluated multiple LLMs to measure their tendency to generate false or misleading information and explored approaches to minimize these risks in real-world business applications.

Hallucinations remain a major barrier to enterprise AI adoption. They can mislead customers, create compliance liabilities, and damage brand reputation, as demonstrated in recent high-profile cases.

In the study, Opinosis Analytics tested five widely used LLMs using both general prompts and stricter prompts. Results showed that:

• All models exhibited some degree of hallucination
• Stricter prompts reduced hallucinations but did not eliminate them
• Larger models delivered more consistent and accurate performance

"LLM hallucinations are a reality that every organization must plan for when building AI systems," said Kavita Ganesan, CEO of Opinosis Analytics. "Our findings show that businesses must incorporate fact-checking pipelines, carefully engineered prompts, and human-in-the-loop oversight as part of their AI strategy development to ensure reliability and trust."

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The full study, including details of the experimental setup, results, and mitigation strategies, is available at: https://www.opinosis-analytics.com/blog/llm-hallucinations/

For more information on Opinosis Analytics and its AI consulting services, visit www.opinosis-analytics.com.