Quick Takeaways
- Give each model a clear job: GPT-Live listens and speaks; Decision-1 tracks what’s known and chooses what to do next.
- Define evidence and outcomes: For each inspection requirement, the model returns ready, issue, or needs evidence—plus the next step.
- Keep decisions grounded in the whole conversation: Corrections can reopen checks, while unrelated valid findings stay intact.
- Turn decisions into natural follow-ups: In the rental check example, the assistant asks about missing evidence, waits when checks are blocked, and finishes only when every requirement has an outcome.
Give the Assistant a Clear Next Move
Live speech rarely arrives in neat order. A cleaner might report a working door code, skip the sink, then correct an earlier detail about the bed. A voice assistant needs more than fluent replies. It must track what the conversation proves—and what remains uncertain.
A decision model can make that judgment. The app supplies the conversation and defined choices. The model returns a status, such as “ready,” “issue” or “needs evidence,” plus a useful next step. Then the voice model turns that choice into a brief question or response.
Separate Judgment From Conversation
In the rental-inspection example, GPT-Live handles listening and speaking. Decision-1 checks four requirements: the entrance, bedroom, bathroom sink and kitchen. A Python app connects the two models and sends them the ongoing conversation.
That division helps the assistant adapt. If the kitchen remains inaccessible, it can check the bathroom instead. If water has been shut off, it can wait. And if a cleaner corrects a report, the app can reassess that detail without discarding unrelated evidence. Importantly, “finish” means every requirement has an outcome—not that every check passed.
Adopt Carefully, Not Blindly
This approach suits repeated tasks with clear evidence rules. It can reduce needless questions and give the app structured answers it can act on. However, teams must define what counts as proof and test how the system handles corrections, vague reports and changing conditions.
Speech recognition can miss words. A decision model can misread context. Therefore, keep the allowed choices narrow, review uncertain outcomes and protect conversation data. Start with one recurring decision. If the model’s choices remain useful in real conversations, expand from there.
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