Jev versus text-generating models
Jev does not generate free-form text. A side-by-side video compares parallel decision-making with token-by-token text generation.
Jev replaces RAG with a fast decision tree
Jev identifies relevant documents directly from user queries using a fast decision tree, eliminating embeddings and vector search for precise LLM context.
JEV just replaced RAG for us! 1. Jev reads the user query and identifies which docs are most relevant. 2. Only that relevant context is served in the LLM query. No embeddings, no vector search! Basically, Jev's quick decision tree replaces the slow, unreliable vector search.
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Pricing, evaluations, and the name Jev
The launch thread explains code-and-AI workflow evaluations, the input price announced at launch, and free output tokens.
Jev Model: Decision-Making Probability Generation, 200× Faster and 400× Cheaper Than LLMs
This model is built for decision-making rather than text generation, outputting probabilities for each option in parallel instead of token-by-token responses. The author shares a simplified comparison of normal LLMs versus this Jev model.
