What is Jev and How to Use It with an LLM? (Free Lab)

Jev is a new AI that is making news and it doesn't chat. You can use it do small classification tasks. In this video, we take one real support ticket from a customer named Alan and follow it all the way through: what we send Jev, what comes back, and how the program decides what to do with the answer. We are not treating Jev as a replacement for your LLM. Instead, we look at where Jev fits inside a real AI agent. The LLM writes the replies, and Jev makes the small decisions around that workflow: which LLM should answer, whether a refund needs a human, and whether a reply follows your policy. You'll also see where Jev is the wrong tool, and when a plain if statement in your code does the job better. The video comes with a free hands-on lab where you call the real Jev model. You'll send a real ticket and read the probabilities that come back, watch a zero threshold misroute a ticket and fix it with one line, see what a missing "other" option does to Jev's answers, put a human in front of a refund, compare Jev with an LLM on the same question, and finish in shadow mode by picking your own threshold from real data. 🧪 Free hands-on lab: https://kode.wiki/3VWbmKV 📚 What you'll learn: 1️⃣ What Jev is, and why it works like a smart if statement instead of a chatbot 2️⃣ Why you can't just use GPT or Claude for these calls, and how Jev differs from a trained ML classifier 3️⃣ The three question types (Noul, Choice, Score) and how to read probabilities and confidence 4️⃣ What Jev costs, and how to set a confidence threshold you can trust 5️⃣ How Jev and an LLM work together inside an AI agent, and when your code should decide instead ⏰ Timestamps: 00:00 - What Is Jev? 00:40 - Jev as a Smart If Statement 02:18 - Why Not Just Use GPT or Claude? 03:11 - Jev vs. a Traditional ML Classifier 04:49 - One Real Support Ticket 05:04 - State and Questions 05:28 - Noul, Choice and Score Questions 07:57 - Reading Jev's Output 09:04 - Your Code Acts on the Numbers 09:53 - What Jev Costs 11:09 - When the Answer Is a Close Race 11:57 - Confidence Thresholds 13:12 - Can Jev Be Wrong? 13:44 - How Jev Works with an LLM 14:04 - What an AI Agent Actually Is 15:46 - Decision 1: Which LLM Should Reply 16:43 - Decision 2: Should the Refund Need a Human 17:52 - Decision 3: Checking the Reply 18:27 - When Your Code Should Decide, Not Jev 19:30 - Where Not to Use Jev 19:52 - Rolling Out Jev in Shadow Mode 20:28 - My Take 20:40 - Free Hands-on Lab 🧪 Free hands-on lab: https://kode.wiki/3VWbmKV 🔔 Subscribe for more AI engineering, system design and real-world backend patterns #Jev #TypeSafe #AIAgents #LLM #AIEngineering #Claude #ChatGPT #MachineLearning #AIForDevelopers #CustomerSupport #KodeKloud #SystemDesign #BackendEngineering #GenAI #AIArchitecture