Determinism and confidence

What is deterministic, what is probabilistic, and how to use confidence here versus in Jev.

What is deterministic

The model. aityx does not answer from a general-purpose model on each call; it answers from a decision model built for your specific problem. A supplied decision definition is a fixed set of rules and expressions executed by a decision engine. The same definition and validated inputs give the same answer and the same rule trace. Each execution still gets its own receipt, so you can tell the calls apart.

What is probabilistic

AI can vary in two places:

1. Reading text. When the state is text, the reader establishes each input with a probability. A fact read at 0.8 carries uncertainty into the decisions that depend on it. The engine can evaluate alternative input values; the receipt lists the distributions used. The answer’s confidence need not be 0.8.
2. Writing the model. A generator is a language model. Two generations from the same prompt can produce different models. Keep the definition you reviewed and submit it directly to remove generation from execution. Repeating questions may reuse a cached model, but cache eviction can trigger a new generation.

Confidence in answers

When every input is given as matching structured data, confidence is 1 and the probability mass is on one answer. Choice responses can still include the other options with probability 0. When the reader had to extract an input, confidence is the probability of the reported answer under the reader’s uncertainty. A noul answer is the probability that the boolean decision is true.

Confidence is not permission

As with any decision system, hard controls on money movement, data access and irreversible actions belong in your application. What aityx adds is that the policy itself is explicit and traceable, so the control you write can point at the rule it enforces.