The number one objection we hear: "But what if it makes things up?" It's a fair concern, and a solvable one. Trustworthy LLM automation is an engineering discipline, not a leap of faith.
Ground the model in your data
Don't ask a model to recall facts from training. Retrieve the relevant documents and have it answer from those, with citations. This alone removes most damaging errors.
Build an evaluation harness
Before anything touches a customer, test it against a set of real examples with known-good answers. Track accuracy over time. If you can't measure quality, you can't trust it, or improve it.
Keep a human in the loop where it counts
For high-stakes outputs, the model drafts and a human approves. For low-stakes, high-volume work, let it run with monitoring and clear fallback rules. Match the control to the cost of being wrong.
Do these three things and "what if it hallucinates" stops being a blocker and becomes a managed risk.