10 things about controlling AI and their hallucinations

The question isn't whether your AI will hallucinate. It's whether you'll catch it before it reaches a customer.

Every retailer using AI for content has run into it. A product description that invents a specification. A campaign text that contradicts your brand guidelines. A price that's just wrong.

These aren't bugs in the traditional sense. They're a feature of how language models work – predicting the most probable next word rather than the most factually correct one. Once you understand that, you stop trying to fix AI and start architecting the environment it operates in.

That's what this paper is about. Ten things that actually work when you're deploying AI at enterprise scale – written for the people responsible for making it work in practice, not just in principle.

What's inside?

Why context is everything – and not all context is equal. How to build human-on-the-loop systems that scale. 

Why your training data is only half the battle. The difference between creative and factual hallucinations, and why treating them the same is where things go wrong.

And why the enterprises winning at AI aren't the ones who've eliminated hallucinations. They're the ones who catch them before they cause damage.

👉 Open the white paper