A few months ago, we wanted to build a context-aware AI coach, one that could tailor advice based on what screen you're viewing in the WHOOP app. Sleep screen? Recovery tips. Activity screen? Training guidance. Sounds simple, but prompt management was becoming a bottleneck.
The prompts couldn't resolve logic on their own, so logic had to either be wired up in code or left up to the model to figure out: "You are WHOOP, a personal wellness and fitness assistant. If the member is looking at Sleep, do X. If they're looking at Strain, do Y..."
What if the prompt itself could be dynamic? What if we could write modular, reusable prompt components that assembled themselves at runtime?
That's HPML (Hyper Prompt Markup Language), a templating language purpose-built for dynamic, composable LLM prompts.