Comment by mholt

LLMs are generalized token predictors. They generate. Jev is a generalized classifier. It does not generate. It computes probabilities, REALLY fast.

So inputs and outputs of LLMs are tokens. Inputs to Jev are state (arbitrary strings/tokens) and, depending on the type of query, either an assertion, options, or choices. (All of those are also arbitrary strings/tokens). Outputs from Jev are probabilities. If it's an assertion, the probability that it is true. For options and choices, it's probabilities for each one, basically.

Because Jev answers so quickly and inexpensively, it's a likely replacement for complex, best-effort functions like `isSpam()`, where up until now the only nondeterministic way of implementing that was an LLM, which is slow, costly, and may produce invalid/corrupt output.