If you want to optimize your SaaS applications, there are several methods to limit the potential of AI hallucination. Here are some practical steps you can take:
If you're seeking guidance on choosing the best restaurant from an AI-powered list in your neighbourhood, avoid adding preferences. For instance, if you highlight that you like Indian food, the system is more likely to favour that option. Similarly, if you suggest an incorrect answer, your algorithm may take that as a shortcut.
Dependence only on model parameters might fall short, especially when the system is handling niche topics or complicated cases. Always try to provide the AI with as much context as possible. Take this scenario as an example: if you need the system to discuss an article, allow it to access the article's content. Having access to detailed context makes the AI less likely to hallucinate.
Even with rich context, the system might still hallucinate, especially if the subject matter is not found in the data. To counteract such behaviour, infuse your model with source identifiers, like the link to the article page. By asking the system to list the sources it used to generate its answer, you can monitor each page and verify their existence.
Simply confirming the existence of sources may not be sufficient. It's a good practice to cross-check the output references against those provided in the context. Using web page links as an example, you can extract the links from the AI’s responses using regular expressions and verify if all of them were initially in the context. If not, the answer is likely a hallucination.
Minimising AI hallucinations, involves avoiding preemptive suggestions, providing ample context, incorporating source identifiers, and cross-checking sources. Employing these tactics should increase the effectiveness and reliability of your system. Rest assured, AI-induced hallucinations will diminish significantly as you incorporate these strategies.
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