Complete Jev AI & TypeSafe AI Course: Beginner to Advanced

This course covers the practical use of artificial intelligence.

Welcome to the Complete Jev AI & TypeSafe AI Course: Beginner to Advanced.

Jev introduces a new approach to artificial intelligence. Instead of focusing on unrestricted text generation, Jev is a System One AI model designed for fast, structured and probabilistic decisions that software can use directly.

In this course, you will learn the fundamentals of Jev AI, TypeSafe AI and System One Models and progressively build real applications using typed decisions, AI agents, RAG, guardrails, intelligent routing, automation and hybrid AI systems.

The course combines theory, demonstrations, hands-on coding and complete projects.

What You Will Learn

You will understand how Jev differs from traditional LLMs, rules, embeddings and machine learning, and when each approach makes sense.

You will work with Jev's main decision primitives:

Noul — binary decisions with probability
Choice — classification and option selection
Score — ranking, severity and structured evaluation

You will also learn how to design better AI decisions using state, questions, criteria, probability, confidence thresholds and Decision Engineering.

Jev API and Development

You will integrate Jev into real applications using:

• cURL

• Python

• JavaScript

• TypeScript

• Jev API

You will learn how to handle requests, responses, API keys, environment variables, errors and reusable integrations.

Jev + LLMs and AI Agents

Jev does not need to replace models such as ChatGPT, Claude or Gemini.

You will learn how to combine System One AI with LLMs to build hybrid architectures for:

• LLM routing

• model selection

• confidence-based decisions

• human fallback

• cost and latency optimization

• output verification

You will also explore Jev inside AI agent architectures, including tool selection, skill routing, risk detection, guardrails, action verification and human escalation.

The course also covers integrations and use cases involving Claude Code, Codex and Cursor.

RAG, Search and Guardrails

Learn how Jev can improve Retrieval-Augmented Generation (RAG) through relevance evaluation, filtering, re-ranking, citation checking and context selection.

You will also explore moderation, AI guardrails, structured verification and safer automated workflows.

Real Jev AI Projects

You will build complete projects, including:

Smart Customer Support Triage
Classify intent, urgency, frustration and cancellation risk.

Intelligent AI Model Router
Route requests between AI models based on complexity, type and risk.

Safe AI Agent Harness
Combine intent detection, model selection, tool routing, risk checks, verification and human fallback.

Reliability and Production

You will also learn how to evaluate and improve Jev systems using:

• accuracy and calibration

• confidence thresholds

• evaluation datasets

• regression testing

• failure analysis

• monitoring

• fallbacks

• privacy

• model versioning

• production reliability

Who Is This Course For?

This course is designed for developers, AI enthusiasts, entrepreneurs, automation professionals and anyone interested in modern AI systems.

You do not need to be an AI expert. We start with the fundamentals and progressively move toward advanced integrations and real-world architectures.

By the end of the course, you will understand how to use Jev AI, System One AI, typed decisions, AI agents, RAG and hybrid AI architectures to build structured and reliable AI-powered applications.