When you use a chat-optimized LLM for classification, routing, and guardrails, it returns a string that you then have to parse again. This comes with hallucinations, type drift, delays of several seconds, and output-token costs. This course follows the architecture of Jev, TypeSafe’s first System One model: provide a state, and it returns typed decisions within a schema along with calibrated probabilities. The model does not generate strings. It covers, through concepts and code, why parallel sampling and RLCD (Reinforcement Learning for Calibrated Decisions) make those decisions fast, how to combine Choice · Score · Noul in a single request, and a hybrid approach that leaves structurally defined judgments to Jev and open-ended descriptions to an LLM.

An AI that understands Korean well, handles long documents, and even carries out real-world tasks—that is exactly the direction Solar Open 2 is aiming for. This class provides an accessible explanation of Upstage’s Solar Open 2 technical report in the order of “Introduction → Design → Training → Evaluation.” Technical terms are explained through everyday analogies and visual materials, enabling even beginners to understand “what,” “how,” and “why” as one coherent flow.

Many people use ChatGPT and frequently hear about recommendation systems and autonomous driving, but when asked to explain “why they work the way they do,” they often struggle to find the words. If you only follow coding courses, the code runs but the underlying principles don’t stick. If you only take math courses, you see the formulas but can’t connect them to AI. Overseas courses can feel intimidating because of the English, while introductory courses in Korea tend to focus heavily on hands-on practice. While building real models in AI competitions, running Everyone’s AI, and meeting thousands of learners, I repeatedly saw the same thing: the obstacle wasn’t how to use the tools, but the moment when functions, derivatives, vectors, and backpropagation became disconnected from one another. This course bridges that gap. From how functions define inputs and outputs, to how derivatives determine the direction of learning, and how backpropagation passes errors backward, it explains everything in one continuous flow—from the fundamentals of mathematics to neural networks and softmax. Rather than memorizing formulas or simply copying code, this course helps you understand “why” through equations and intuitive illustrations. For anyone who wants to truly understand AI, it will serve as a solid first step toward moving on to deep learning.