Five stages of building with LLMs, and why most teams are solving the wrong one.
You've shipped an LLM feature. It works in demos and fails in production. The fix isn't a better model. It's figuring out which stage you're actually in.
Three ideas that replace prompt-tweaking with a real mental model.
Models are functions. Models are stateless. Behaviour comes from context. That's the whole foundation, and it explains why your prompts keep failing unpredictably.
Don't teach something until it's been around for five years.
GPT-3 turns five this year. Long enough for the hype to settle and the real failure modes to surface. Which is exactly when we built this workshop.
Small teams are forced to understand the entire system.
A small team can't hide behind process, they have to know the user, the data, the edge cases, and the failure modes. That belief shapes how I think about building with AI.