Six production patterns for Jev, from semantic SQL to cascade safety
Treat Jev as a decision layer rather than a chatbot, with six patterns: semantic SQL, tool routing, RAG gating, entity resolution, text-to-features and cascade fallback.
JEV HAS 6 PRODUCTION PATTERNS HIDING INSIDE ONE API AND NONE OF THEM NEEDS ANOTHER CHATBOT six narrow systems where a probabilistic decision becomes useful data 01 SEMANTIC SQL text → scored columns → normal queries 02 TOOL ROUTING request → shortlist skills → stronger agent chooses 03 RAG GATES retrieve → judge the evidence → keep or reject passages 04 ENTITY RESOLUTION code finds candidates → Jev scores likely matches 05 TEXT TO FEATURES reviews → probabilities and scores → CatBoost learns the final policy 06 CASCADE SAFETY cheap decision first → uncertain tail to a frontier model or human the production loop is the part worth stealing retrieve narrowly → ask atomic questions → batch questions sharing one state → calibrate thresholds on labeled examples → escalate uncertainty → log every correction Jev should not count, compare dates, perform arithmetic or authorize irreversible actions by itself code owns exactness Jev owns semantic uncertainty a stronger model enters only when confidence earns the cost the best Jev product may not look like AI at all it may look like one extra column telling ordinary software what deserves attention save this before turning another workflow into a chatbot
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