JEV: Building Fast, Structured System-1 AI

Note & Mandatory: It's mandatory to both RSVP and fill this Google form https://forms.gle/Gw5wGiLubwhE5u2W9 if either is not done, security won't allow at the venue.

Large language models are powerful, but many real-world AI tasks do not need long reasoning chains. They need fast, predictable, structured decisions.
This hands-on workshop introduces JEV, a new class of System-1 AI models designed for tasks such as classification, extraction, routing, validation, scoring, and structured prediction.
We will start from first principles and understand where JEV fits compared with traditional classifiers and LLMs. Then we will build practical applications where an input is converted directly into a structured output with labels, fields, and confidence scores.

### What we will cover

• System-1 AI vs System-2 reasoning
• What JEV is and where it fits
• JEV vs traditional classifiers vs LLMs
• Schema-driven structured outputs
• Classification, extraction, routing, and validation
• Confidence scores and decision thresholds
• Using JEV inside AI agents and application pipelines
• Real-world examples:
• Resume ↔ Job Description matching
• Support-ticket routing
• Content and document classification
• Enterprise data validation
• Browser and social-media filtering
• Building an end-to-end JEV application

### Workshop Mental Model

Input → JEV → Schema + Prediction + Confidence → Application Decision
By the end of the workshop, participants will understand when to use a JEV-style System-1 model instead of a large reasoning model and will build a working application around it.