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Jev is the if statement of AI

You would never call an LLM to compare two numbers. Yet that is roughly what we all do with AI right now: we hand a model that could draft a legal opinion a question with two possible answers, and we pay it to write a paragraph. Recently, a model arrived that is built for exactly that question. It is called Jev, and to us it is the if statement of AI. The branch you can't write down "Does this…

For decades, AI has been likened to an "if statement." With each new model, the comparison intensifies. Jev, from TypeSafe AI, is regarded as the quintessential AI "if statement." Its purpose is to serve as a branch that cannot be written down but must be addressed through a model. The ability to discern whether an email is a complaint or a change request, or whether a legal text belongs to the brand's assets, lies within Jev's capabilities.

Unlike traditional LLMs, Jev generates no text. Instead, it receives unstructured input and a predetermined set of answers, returning one with a calibrated probability. What sets Jev apart is its speed and cost-effectiveness. It is 194 times faster and 445 times cheaper than an LLM for similar tasks. The implementation of Jev involves a confidence gate, where a probability determines the path an email takes—either directly to quality assurance or to a customer service representative for further review.

While Jev is a valuable tool, it is not without limitations. It cannot write replies to customers or provide reasons for its choices. However, its ability to provide calibrated probabilities and the option for human intervention when uncertain makes it a promising advancement in the field of AI. Currently, Jev is in early access through a waitlist.

As language models continue to evolve, the integration of Jev-like systems may become more prevalent, with LLMs focusing solely on generating content while Jev handles the decision-making process.

Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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