One of our goals with FocalPrompt has been to make prompts dynamically composable: treat tagged prompt components (“foci”) almost like tool calls or retrieval, and only include the instructions relevant to the task at hand (in our case drafting responses to a range of pet owner queries). Until now, doing that has usually meant extra model calls → extra latency + cost. There’s also a harder problem: the foci a model predicts it needs aren’t always the same ones it actually appears to rely on. Leave-one-out ablation can reveal that gap. Two days experimenting with Jev looks very promising. Score each focus → set an inclusion threshold → compose the prompt dynamically. The speed and cost make per-focus routing look genuinely practical. The interesting question now is whether predicted relevance reliably preserves the instructions the agent causally depends on. @willmonk @GetPetsApp