At a previous job, I worked on a custom promotional deals engine
It was one of the most complicated and fragile systems in the codebase, and I kept thinking there had to be a better way.
Promotional deals look simple from the outside, but the parameters multiply fast: quantities, brands, categories, logic conditions, subset pricing, exclusions, stacking rules. Every new campaign meant engineering work. Every edge case meant more code.
The insight...
This isn't actually a hard problem for a human.
Show a person a cart and a deal in plain English, and they can tell you in seconds whether it applies and how much to discount. The difficulty isn't the logic, it's translating fuzzy language into formal rules. Which is exactly what LLMs are good at.
So I built a prototype. Send Claude a cart and a deal description, and it returns whether the cart qualifies and the discount amount. It works, even with vague phrasing.
The payoff isn't really about saving engineering time. It's independence from a rules engine vendor, speed to launch new deals, and non-technical teams that can build and ship on their own. It's also a template for how to think about AI optimization more broadly: a lot of the rigid systems we've built over the years were rigid because natural language wasn't a viable runtime. That constraint has quietly lifted, and it's worth looking around for other places the same shift applies.