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🧩 AI Models & Concepts Explained

New AI models and ideas show up every week. This page collects explainers on the ones worth knowing: what each one is, how it differs from the LLMs you already use, and where it fits in real testing and automation work.

📋 Table of Contents​

  1. Decision Models: Jev

Decision Models: Jev​

LLMs like GPT and Claude generate text. Sometimes you don't need text, just a fast, structured decision. Jev is a decision-focused ("System 1") model that returns a choice, score and probability in milliseconds. It sits alongside LLMs rather than replacing them.

What is Jev? System 1 AI Explained​

How Jev differs from traditional LLMs, what System 1 AI means, and why choice/score/probability outputs can be faster and more reliable. Includes a QA example: spotting flaky tests and using confidence scores to decide whether to retry.

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Jev in Action: 5 Practical QA Use Cases​

The hands-on follow-up: get access, configure the API key, then classify failed Playwright tests, use Jev as a guardrail, combine it with RAG, and call it through an API from Postman/Hoppscotch and from code.

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🎯 Key Takeaways​

  • Not every AI task needs an LLM. Use LLMs for generation and reasoning, and decision-focused models for fast, structured decisions
  • Structured outputs (choice, score, probability) are easier to automate on than free text
  • Confidence scores let you act differently on sure and unsure results, such as retrying a possibly flaky test

🚀 Next Steps​

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