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Generative AI (GenAI) sometimes gives inconsistent answers to the same question – a problem known as hallucination. This occurs when an AI chatbot lacks context or has only initial training, leading to misunderstandings of user intent. It’s a real-world problem – an AI chatbot could make up facts, misinterpret prompts, or generate nonsensical responses.
According to a public leaderboard, GenAI hallucinates between 3 to 10% of the time. For small businesses looking to scale with AI, this frequency is an operational risk.
GenAI hallucination is no joke
Small to medium-sized businesses need accurate and reliable AI to help with customer service and employee issues. GenAI hallucination affects different industries in unique ways. Imagine that a loan officer at a small bank asks for a risk assessment on a client. If that risk assessment regularly changes due to hallucination, it could cost someone their home.
Alternatively, consider an enrollment officer at a community college asking an AI chatbot for student disability data. If an identical question is asked and the AI provides an inconsistent response, student well-being and privacy are put at risk.
Hallucinations cause GenAI to make irresponsible or biased decisions, sacrificing customer data and privacy. This makes Responsible AI even more important for medical and biotech startups. In these fields, hallucination could harm patients.
Counteracting the issue
Experts say a combination of methods – not a singular approach – works best to reduce the chance of GenAI hallucinations. Advanced AI platforms take the first step to improve chatbot reliability by merging an existing knowledge base with Large Language Models. Below are further examples of how AI technology can mitigate hallucination:
- Prompt tuning - an easy way to get an AI model to do new tasks without having to re-train it from scratch.
- Retrieval-augmented generation (RAG) - a system that helps the AI make better, more informed decisions.
- Knowledge graphs - a database where the AI can find facts, details, and answers to questions.
- Self-refinement - a process allowing for automatic and continuous improvement of the AI.
- Response vetting - an additional layer of the AI self-checking for accuracy or validity.
A recent study noted more than 32 hallucination mitigation techniques, so this is a small sample of what can be done.
GenAI hallucinations are a dealbreaker for small businesses and sensitive industries, which is why great Advanced AI platforms evolve and improve over time. The Kore.ai XO Platform provides the guardrails a company needs to use AI safely and responsibly. With the right safeguards in place, the potential for your business to grow and scale with GenAI is promising.
