Introducing Jev

Diogo Almeida, introducing Jev:

While Jev gives up string generation, it’s optimized for structured outputs and can’t hallucinate.

The discussion around Jev has resurfaced how capable older ML approaches remain. For a well-defined task, a fine-tuned BERT classifier can be faster, cheaper, and more accurate than a general-purpose LLM. It can choose the wrong label, but it can’t invent a new one.

But building that specialized solution takes work. LLMs offer useful results across an extraordinary range of tasks without requiring a separate training project for each one. If an LLM already does the job well enough, the specialized alternative has to justify the time and effort needed to build it. That effort is part of what makes a solution better.