Definition
Deterministic vs. generative voice AI is the distinction between call steps that run as fixed, scripted logic and behave identically every time, and steps where a large language model generates responses that can vary for the same input.
Generative models make open-ended conversation possible: they can interpret a free-form answer and decide what to say next. That flexibility is probabilistic by design. Forrester's 2024 AI Pulse survey found roughly 70% of U.S. generative-AI decision-makers believed a generative model always returns the same output for the same prompt, which it does not.
Some moments on a patient call cannot vary: the AI-voice disclosure, consent capture, identity verification before any PHI is spoken, opt-out recognition, and structured clinical screening. A system that handles those correctly 99% of the time still misses thousands of times across a large program, and each miss can carry per-call statutory damages. The working pattern is to run those steps deterministically and let generative AI handle the parts that benefit from sounding natural, such as acknowledging a frustrated patient or finding a callback time.
The choice extends to data, not only words: in some states, PHI and biometric identifiers may not be permitted to reach a general-purpose model at all. Matching the conversational technology to each step is a design decision that has to be revisited as the rules change.
How Consig handles it
Consig workflows mix deterministic and generative stages, built in its UI or configured via JSON. Consig orchestrates deterministic dialogs, NLU, and generative AI, giving each component only the data it needs.