# Jev Explained for Python Developers

## Executive summary

This video provides a deep dive into TypeSafe's Jev model, a novel classification model designed for building reliable, structured AI applications. Jev moves beyond traditional function calling by offering specialized methods—Choice, Score, and Null—to classify inputs. For build engineers, the key takeaways are the model's ability to facilitate complex decision-making (if/else logic) through structured API calls, coupled with significant performance advantages, being notably faster and cheaper than competitors like Claude Haiku.

## Key takeaways

- Jev: A New Classification Paradigm: Jev is presented as a new model category, optimized specifically for classification, which is a critical component for building reliable LLM-based systems. It is designed to be declarative, simplifying the need for complex system prompts.

## Technical details

- Jev Model Mechanics (Choice, Score, Null): The model supports three primary classification types: **Choice** (selecting from a predefined list of options), **Score** (providing a probability distribution across multiple criteria), and **Null** (a boolean, yes/no classification, providing a probability score for 'yes').
- Implementation and API Structure: The model is accessed via the Python SDK using `client.system_one()`. The API is designed to handle multiple question types (Choice, Score, Null) in a single, combined API call, allowing for comprehensive data extraction from a single input state.
- Performance and Cost Comparison: Jev is highlighted for its speed and cost efficiency. The speaker claims that running Jev can be significantly cheaper (e.g., 25x cheaper) and faster than using models like Claude Haiku for similar classification tasks.

## Practical implications

- The model enables building highly reliable, programmatic workflows by allowing developers to use structured classification (Choice, Score, Null) to drive complex if/else logic and decision branching.
- Combining multiple classification questions (e.g., Choice + Score + Null) into one API call streamlines system design and reduces ambiguity compared to sequential calls.
- The superior cost and latency profile of Jev makes it highly attractive for production-grade, high-volume AI applications.

## Topics

AI, LLMs, Classification, API Design, Python SDK, Build Engineering, AI Cookbook (Python Examples), TypeSafe Documentation

Source: https://www.youtube.com/watch?v=JpfLID19QnQ