The Challenges of Prompting Large Language Models

Use cases that have anything to do with compliance or regulation are just a no go because of the hallucinations and trust issues here. Exploring latent space through prompts feels like we've probably got a better way to go about that also when it comes to prompts is there a nuanced way of getting better output? Some teams fear the model is hallucinating and the company may not know what data was used to train these models. The smaller models are easier to control as far as output goes but they're not perfect so how can we monitor the outputHow can we make the output secure and how can we get that consistency in quality?"