> I strongly doubt that a copycat that was put together within days after Jev’s release will be able to match it on a sun of its properties
But why? If the simplest way to achieve Jev's capabilities (accuracy, cost, latency) is by fine tuning a small model, what makes you think that this isn't exactly what Typesafe did?
And even if they did something different - what makes you think it was a good idea in the first place, given how easy their results were replicated without any "secret sauce"?
My point is that it’s not replicated. You replicate the accuracy, but not the other properties. The Jev-competitors only proved that you can get or beat the accuracy, nothing about the other properties. Especially the “zero hallucination” output and the (if it works how the documentation make it sound) prompt injection resistant architecture. You can’t get that with a fine tuned LLM.
> zero hallucination
Plenty has been said about this claim. If you're still falling for this, I feel sorry for you.
If you remove wheels from your car, your car will get a "no speeding ticket" property, and yet there's nothing exciting about it.
> You can’t get that with a fine tuned LLM
Of course you can. All these claims are nothing but marketing.
I understand most major model providers support passing a JSON schema that is strictly followed in the output, accomplishing the same 'zero hallucination' and prompt injection resistance.
The only difference I am aware of is that probabilities are better calibrated with these decision models compared to regular LLMs which can output hallucinated numbers where your schema allows a number.