The Jev adapter classifies prompts before they reach the LLM, reducing token consumption and costs. It provides classification and confidence scores, marking prompts for review when confidence is near the threshold.
added by @_mustafakarakus
Ranked from stored criteria vectors. No live classification on this page.
Jev offers a new intelligent decision-making primitive that enhances model routing and classification tasks. It allows for quick and cost-effective decision-making, improving the efficiency of LLM calls in applications.
Jev, a new decision model, demonstrated a routing cost 125 times lower than GPT-5.5 when deciding actions for a coding agent after a failed test.
TypeSafe AI's Jev model has been integrated into a system layer, providing clean, typed decisions from unstructured state in under 500ms. This integration enhances backend processes by scoring and filtering context efficiently.
An experiment integrating Jev into a SaaS AI Agent shows remarkable speed, responding faster than user input. It operates at approximately 100 times lower cost than traditional LLMs and efficiently directs requests needing a full LLM.
This plugin transforms model output into typed judgments: Choice, Score, or Noul, ensuring validation before host code execution.
A shared inbox router has been developed using Jev and Mastra technologies.