To effectively limit LLM hallucinations, you need to treat LLMs more like journalists instead of storytellers. Journalists weave stories using factual, real-time information. Similarly, you need to feed your LLMs with real-time information to ensure their generated content aligns more closely with reality. Storytellers on the other hand don't require real-time information, since they are creating tales in a fictional world.

In our experience, LLMs are primarily used to distribute static content, but static content queries only cover about 20% of the answers users seek. The majority of queries require real-time information. For instance, asking for a current cash forecast at 10 a.m. on a given day will have a different answer hours, if not minutes, later. Or, "Which sales opportunities have a chance of slipping into the next quarter?" Answers to these questions reside in your finance and accounting or customer relationship management software systems. You can't train an LLM on this fluid data but you can prompt an LLM with the data from these systems by integrating an LLM with your backend systems. This in essence will limit LLM hallucinations since you are prompting it with your real-time data to generate an answer that contextually makes sense to the person asking the question given they have permission to read the data.

Moreover, when individuals pose questions like these, they often aim to initiate a whole workflow. For instance, the sales opportunity question mentioned earlier about which deals may slip, cannot be resolved by referencing static content. Such requests will involve triggering other systems or human workflows that fall outside of an LLM's purview. A sales manager or chief revenue officer will seek to initiate some type of action if deals are slipping so they can maintain the sales forecast. They may want to offer a discount to accelerate a deal or offer an incentive if excess inventory is available.

It's essential to realize that while LLMs are an important part of the solution, they are not the entire solution. To handle queries related to static content, real-time information, and integrated systems or workflows, you need to marry LLM capability with other systems. With this integrated approach, you can limit LLM hallucinations, ensuring the AI system provides more accurate and beneficial responses.

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