What Is an LLM Hallucination and Why Does It Happen?
An LLM hallucination occurs when the model “perceives patterns or objects that are nonexistent or imperceptible to human observers, creating outputs that are nonsensical or altogether inaccurate,” according to an IBM blog post.
Generative AI LLMs predict patterns and generate outputs based on vast amounts of training data, notes Huzaifa Sidhpurwala, senior principal product security engineer at Red Hat, who has written about hallucinations. LLMs, he says, “excel at mimicking human-like communication and producing contextually relevant responses,” but hallucinations are a “critical limitation” of the technology.
Huzaifa Sidhpurwala, senior principal product security engineer at Red Hat, says that LLMs are trained to predict the next word in a sequence, not to fact-check. That means when they lack reliable knowledge, they may confidently invent details that look legitimate but aren’t.
For small businesses that may be leaning on AI for efficiency, it’s important to understand this limitation before trusting AI responses in areas such as customer communication, contracts or technical documentation.
Best Practices for Small Businesses to Prevent LLM Hallucinations
The good news is that SMBs don’t need to abandon AI. Instead, IT leaders can adopt practical safeguards:
- Use responsible AI principles. Adopt the same pillars large enterprises use: transparency, explainability, inclusivity and sustainability. Even on a smaller scale, these help guide safe AI use.
- Consider smaller, domain-specific models. Instead of relying solely on massive, internet-trained tools, explore small language models trained on your business’s verified data. These models are easier to manage and often more accurate in your niche.
- Leverage retrieval-augmented generation. Ground your AI in your own knowledge base (such as product manuals, policies or customer FAQs). This reduces hallucinations and ensures responses align with your business.
- Always keep a human in the loop. Make sure AI outputs are reviewed before sharing with customers or being used for critical decisions. For lean IT teams, this can be as simple as designating a single checkpoint process.
“Above all else, using trustworthy data with a strong provenance is vital,” Sidhpurwala emphasizes.
DIG DEEPER: How to train your artificial intelligence bot with chain-of-thought prompting.
