Classify data with an AI decision model
When a decision model beats a chat model for classification TL;DR For classification, use a decision model , not a chat model. A chat model writes text you then have to parse. A decision model, like Jev , answers each yes/no question with a probability: no text to generate, nothing to parse, and a number you can set a threshold on. Ask one question per label, keep the labels above the threshold,…
When a decision model outperforms a chat model for classification purposes, it's clear to see the benefits of choosing the appropriate tool. While a chat model generates text, requiring parsing and having no inherent confidence measurement, a decision model like Jev from TypeSafe offers a more efficient solution. With Jev, each yes/no question is answered with a probability, allowing for easy threshold setting and immediate labeling without parsing or generating text.
Labeling a todo app with groceries, chores, work, or none of the above becomes faster and more cost-effective using a decision model. Simply add a tags column to your database, define the labels and associated yes/no questions, and send all questions in one request to Jev. The model's calibrated probabilities provide real certainty, making it an ideal choice for automatic classification.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
