Jev as a Routing Layer: The 'What If' That's Already a Documented Pattern
A concept I find interesting is Jev as a fast decision & routing layer.
Instead of:
User → LLM → Decide → Execute
Think:
User → Jev → ML / LLM / Tools → Action
🎯 Jev could handle things like:
- 🔍 RAG routing: keyword vs semantic vs entity search
- 📄 Document routing: selecting the right ML pipeline
- 🛠️ Agent tool selection
- 🚦 Confidence-based escalation from ML → LLM → human
The idea isn't to replace ML or LLMs.
It's to use the right model for the right job:
⚡ ML → prediction
🧠 LLM → complex reasoning
🎯 Jev → fast decisions & routing
⚙️ Tools → deterministic execution
This could mean lower cost, lower latency and more controllable AI systems, particularly for enterprise RAG and agentic applications.
What if not every decision in an AI system needed an LLM? A fast decision-and-routing layer like Jev sits between the user and the rest of the stack — routing to ML, LLM, or tools rather than defaulting every step through a large model.
This maps directly onto real, documented patterns: RAG routing (scoring retrieved passages for relevance before they reach the answering model), pipeline/document routing, tool selection among a known set of actions, and confidence-based escalation, where low-confidence cases get passed up from a cheap model to a stronger one, or to a human, rather than every request going through the most expensive path by default.
The goal isn't replacing ML or LLMs — it's matching the right layer to the right job: ML for prediction, LLMs for open-ended reasoning, a fast decision layer for routing and confidence-gating, and deterministic code for execution. Done well, that division of labor can mean lower cost, lower latency, and more controllable behavior — particularly for enterprise RAG and agentic systems, though the efficiency claims here are still largely vendor-reported rather than independently benchmarked.