You don't really understand AI strategy until you're deep in the chaos of a production launch. Not the theoretical kind you read in papers — the real one. The one made of shifting model updates, hallucination risks, and latent stakeholders who want magic but fear the 'black box'. That's where the real engineering lives. And that's where I've learned the most.
When you're building products on top of Large Language Models, three things decide the outcome: strategy, speed, and systems.
1. Strategy is not a deck
In AI, strategy isn't a 40-slide presentation. It's knowing what NOT to automate. It's understanding that a 95% accurate model in a high-stakes environment is often worse than no model at all. True strategy is choosing the right hook where AI adds 10x value, not just 10% more noise.
2. Speed vs. Systems
Iteration speed is everything. If you can't test a new prompt or model variant in minutes, you're dead. But speed without a system is just a race to a buggy production environment. I've learned that freedom lives inside structure. By building robust evaluation loops (Evals), you gain the confidence to move fast without breaking the core user experience.
I'm talking about automated testing for hallucinations, latency benchmarks, and cost tracking — the boring stuff that creates room for magic. That's how AI products go from 'just a demo' to actually working at scale.