Everything about AI Agent Development Learned by Building a Chatbot (FastAPI, RAG, Vector, LangChain, sLLM/Fine-tuning)

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.

You can learn about Text-to-SQL systems that generate and query SQL statements in real time in response to user questions.

Master the complete lifecycle of building modern Generative AI applications using Large Language Models, Retrieval-Augmented Generation, and Agentic AI systems. Learn to design enterprise-grade AI solutions from prompt engineering to deployment, combining LLMs with vector databases and external knowledge sources for production-ready applications.

Go beyond a simple system prompt to build an AI character chatbot that remembers users and whose relationship with them evolves, using persona design and Structured Outputs. In this 30-minute hands-on session, you’ll learn how to safely call the OpenAI API based on the provided starter project, process emotions, trust levels, and memories in a verifiable format using JSON Schema and Zod, and use an AI coding agent within defined constraints.

Explore how LLM applications are shifting the development paradigm. Understand why LLMs are becoming a sustainable core technology through comparisons with existing technologies, and learn about the essential skills and mindset shifts required for developers. Gain insights from expert instructors to grasp the essence and future direction of LLM application development.

This course is a 37-lesson, approximately 14-hour program that covers OpenAI’s AI coding agent, Codex, from start to finish. AI coding tools are now commonplace. However, most users remain at the level of accepting autocomplete suggestions or asking questions in a chat window. Agentic tools like Codex can do much more than that. They can read an entire project, modify multiple files simultaneously, run tests, and find and fix errors on their own. The problem is that they require an equivalent level of control. If you do not know what to allow and what to block, or how to teach the tool your project’s rules, you will end up cleaning up the code it creates afterward. This course focuses on how to establish that control. In the beginner section, you will learn installation and your first conversations, file editing and session management, and slash commands, while building a foundation in prompt writing. Through hands-on practice, you will internalize three principles: making clear requests, breaking down tasks, and verifying the results. The intermediate section is where the main content begins. You will configure behavior with config.toml, teach project rules with AGENTS.md, and manage memories with Memories. You will design safety boundaries yourself by combining the sandbox with four types of approval policies. The course then covers thread separation and parallel work, planning mode, connecting MCP servers, and creating custom skills based on SKILL.md. Finally, after covering web searches and Git automation, VS Code and Cursor integration, and hands-on FastAPI practice, you will examine Codex’s internal protocol, the SQ/EQ queue model. Understanding how the tool works changes how you respond when problems arise. The entire course follows the process of building a single service called TaskFlow. Rather than learning each feature in isolation, you will experience, in sequence, when and why each one is needed in a real project.

"Your first step to creating a customized LLM with LoRA-based lightweight fine-tuning!" This is an introductory hands-on course designed so that even those new to LLMs can easily follow along. We minimize complex theory and guide you step-by-step through the entire process: loading the model → applying data → training → comparing results. In a short time, you'll directly experience the workflow of cutting-edge lightweight fine-tuning techniques like LoRA and QLoRA, and gain an intuitive understanding of "how LLM fine-tuning works." Even without extensive resources, experience the satisfaction of creating an LLM specialized for your domain!

Solve the point where 80% get stuck at makeathons. Complete Raspberry Pi · Computer Vision · LLM · 3D Design in 4 weeks! Achieve top rankings at makeathons with a demonstrable smart mirror PoC!

This course covers the theory and practical examples of LLMs, based on <Fundamentals of Large Language Models, Quickly!> (Insight, 2025).

Have you wanted to implement RAG but felt overwhelmed by connecting everything from embeddings to vector storage and search? Based on real-world service development experience, this course explains the core concepts of pgvector and the service source code. By calling the API directly, you can quickly understand the entire flow of how RAG vector search works.

We explore how to create an AI judge by combining knowledge graphs, reasoning systems, and various agent frameworks such as CrewAI, LangChain, and AutoGen, which enable the implementation of LLM-based multi-agent technology.

You use ChatGPT, but haven’t you found it difficult to explain why it produces answers like these? “I know terms like RAG, agents, and fine-tuning… but it’s difficult to explain them precisely.” “When I hear LLM-related terminology, I’m at a loss for words.” “Explanations of concepts in AI meetings are always vague.” This course was created specifically for people like you. This is a theory course designed to help you understand LLMs not as ‘tools,’ but as ‘structures.’ Rather than teaching you how to use ChatGPT or Gemini, it gives you a framework for explaining why they work the way they do.

It is difficult to complete a classification service using only AI API calls. Based on practical experience, I guide you through everything from implementation to deployment.

LLM: A pro quickly delivers core content, from essentials to practical tips!

You can enhance your development capabilities by deeply exploring the fundamental principles of large language models like ChatGPT and Claude. You will be able to learn a variety of information and development techniques that are not commonly encountered.

We will explore the types of applications and web services that can be built using LLMs.

Do you still code manually these days? We are now in an era where even "vibe coding" feels like a chore! Become an AI automation expert with n8n, no coding required. A guide to designing automation workflows that can be applied immediately to real-world tasks. Practical automation strategies to maximize work efficiency. Build intuitive workflows with n8n and experience a revolution in the way you work.

If you’ve ever had trouble determining whether the quality of responses declined after changing a prompt or model while building a service using an LLM, this course may be the solution. You’ll learn how to create your own evaluation criteria and automatically identify responses that have changed.

Done in a single line of command. Hermes, the AI agent that remembers, automates, and grows on its own. Even if you are not a developer, you can become someone who 'delegates' work to AI starting today. SMARTFORK will be with you from installation to practical application.

[Source Code Provided] No coding knowledge required. Build it instantly in your browser using Google's latest tool (IDX) without any installation! Stop studying coding syntax! This is an ultra-fast, hands-on course where you'll build an RAG chatbot that perfectly understands internal company documents through AI conversation (Vibe Coding) in the Google IDX environment and deploy it live to the web.