prompt.md

Pace Intent Router — classification prompt

This model is a classifier, not a generative model. It does not use a prompt in the traditional sense. The input is a raw user transcript (byte-level encoded, max 128 bytes) and the output is one of 7 class labels.

Input format

<raw user transcript, byte-level encoded, max 128 bytes>

Output format

<single class label from: chitchat, pureKnowledge, screenDescription,
screenAction, research, phoneLargeModel, unknown>

Class definitions

• chitchat: Greetings, thanks, goodbyes, social filler. e.g. "hi pace", "thanks", "how are you"
• pureKnowledge: Factual questions answerable without screen context. e.g. "what is HTML", "explain DNS"
• screenDescription: User wants a description of what's on screen. e.g. "what am I looking at", "what's on the screen"
• screenAction: User wants Pace to do something via the action layer. e.g. "click the save button", "open Safari"
• research: Multi-step research turn. e.g. "research quantum computing", "compare AWS vs GCP"
• phoneLargeModel: Explicit escalation request. e.g. "use the big model", "phone a large model"
• unknown: Classifier could not confidently assign a class. The caller must run the full pipeline.

Decision boundaries (Pace-specific)

These boundaries are encoded in the synthetic training corpus and are what the model learns. A general LLM does not know these:

• "turn on lights" = unknown (Pace can't control lights)
• "turn on volume" = screenAction (Pace can control volume)
• "what can you do" = pureKnowledge (not unknown — it's a question about Pace itself)
• "research HTML" = research (not pureKnowledge — the word "research" triggers the research lane)
• "I researched HTML yesterday" = pureKnowledge (past tense — not a research request)