| # 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) |
|
|
