I think the point of Jev is to thread the needle of the gap between non-LLM classifiers and LLMs.

Classifiers like classical NNs require:

- annotated data, potentially a lot of it - training - inference

#2 and #3 aren’t a big deal if you have an ML engineer, but #1 will always be a potential headache no matter who you are. The tradeoff is that they could be quite fast, cheap, and you can get probabilities, not just classes.

With LLMs you get:

- zero shot classification (no dataset or training required) - potentially can use third party model providers like OpenAI off the shelf. Don’t even need to host your own model.

The downside to LLMs is that they are comparatively slow and expensive to traditional classifiers. Historically they also were prone to hallucination or malformed responses, though not as much these days. You also can technically get log-probs back, but these aren’t equivalent to the classifier probabilities.

Jev gets you the zero-shot, zero-infra benefits of LLMs, while being closer to the speed and cost of traditional ML classifiers, as well as both classification and probability responses.