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.
added by @MichaelLee04
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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.
The Hermes setup now features 443 unique skills, utilizing Jev for skill selection. Jev achieved 94.4% exploratory reused-data performance, ensuring confidence-aware decision-making.
Jev is a model that processes states and typed questions to return structured JSON with probability distributions. It outperforms previous setups in speed and cost, making it ideal for routing and decision-making tasks.
The integration of Jev from @typesafe_ai replaced three steps in Prio, achieving 100% accuracy in model routing and significantly reducing action review time from 5 seconds to 0.25 seconds for clear cases.
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.