{
  "id": 15128078,
  "title": "Classify data with an AI decision model",
  "url": "https://urgent.news/2026/10/17/classify-data-with-an-ai-decision-model",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-17T05:31:27.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/codegino/classify-data-with-an-ai-decision-model-19p8"
  },
  "original_language": "en",
  "account": "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.",
  "summary": "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,…",
  "key_points": [
    "Decision models outperform chat models for classification tasks",
    "Jev from TypeSafe provides probability-based answers for yes/no questions",
    "Faster and more cost-effective labeling using Jev's calibrated probabilities"
  ],
  "editors_take": "Prioritizing decision models over chat models streamlines data classification workflows by replacing text parsing with calibrated probabilities to increase speed and cost-effectiveness for automated labeling tasks.",
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}