Practical LLM Service Development for Spring Developers

Developing an 'Autonomous' Premium Reservation Agent Backend Using Spring AI + JPA + MySQL Beyond Chatbots to Agents: The Standard of Tool Calling and Prompt Engineering

We introduce basic prompt patterns for coding and advanced API prompt patterns for leveraging artificial intelligence.

I'm learning how to build practical AI agents using LangGraph while working on various AI agent implementation projects using LangGraph.

The limitations of LLMs? I’ll share practical solutions to real-world problems based on RAG trial and error and operational know-how from working in Silicon Valley.

LangGraph, packed with the know-how of an AI Agent lead from a major corporation. I will pass on the knowledge I've gained through trial and error in the field.

We design and implement AI agent systems that go beyond simple chatbots to perform tasks autonomously. From Sense-Think-Act loop-based architectures to LangGraph cyclic structures, RAGAS and LangSmith-based quantitative evaluation, and operational guardrails, you will complete your enterprise-grade agent engineering capabilities through hands-on practice of the entire process.

The most definitive practical guide for developers who want to integrate AI into their Spring projects right away! Beyond simple LLM API calls, you will perfectly master building production-level AI environments that can be immediately applied to real business logic using RAG and MCP.

Design and create your own student-customized AI Tutors and AI Avatars using generative AI such as ChatGPT and Gemini along with Google Colab. From prompt engineering and data analysis to building an AI Career Exploration Platform for career and academic counseling, you will complete practical projects that can be immediately applied in the classroom.

Learn how to see in numbers and graphs how many tokens your Spring AI chatbot and RAG service uses, how slow it is, and how often it fails. Using just Actuator, which Spring developers are familiar with, you’ll extract the metrics Spring AI is already recording and build dashboards with Prometheus + Grafana.

If you want to properly learn OCR technology that's truly used in practice, this one course is all you need! Aiming for over 98% accuracy even with unstructured documents and complex layouts, based on the latest SOTA models and real-world know-how, we build enterprise-level OCR projects together.

From AWS EC2 backend operations to automatic error fixing, we are revealing the entire development process of Sidabari (https://github.com/cx8537/sidabari), a Claude Code-based automation tool. In this course, we provide all the actual prompts and conversation history used as learning materials, allowing you to experience the entire prompt input process involved in its creation.

The "How Far Can You Go with ChatGPT?" series: Creating a Stock Auto-Trading Application and Web Investment Reports!

I highly recommend Spring AI to developers using Spring Boot and React, as well as those considering the introduction of AI systems in corporate environments. This course goes beyond a simple introduction to libraries; it helps you understand the core concepts and internal structure of Spring AI, and strengthens your practical implementation skills through various hands-on examples. The course is based on Spring AI version 1.1.x and provides continuous technical support and expansion strategies to ensure you can adapt to future version upgrades. Furthermore, we expand the practical value of Spring AI technology by utilizing MCP (Model Context Protocol) to achieve cross-domain context integration, AI linkage in distributed environments, and AI architecture design suitable for enterprise environments. With the goal of building AI systems that can actually be deployed in corporate settings, this will serve as a practical guide that balances theory and hands-on experience.

Create a program using the GPT API, and even generate images and videos!

Learn AI utilization methods and performance improvement strategies tailored to real-world business situations. (i.e., you won't learn things like automatic short-form video generation.) This is a practical course where you learn to leverage AI not merely as a tool, but as an ultimate instrument for business efficiency. By utilizing various generative AI and the latest AI agents such as ChatGPT, Gemini, NotebookLM, Gesnpark, Skywork, etc., you will master AI prompt utilization → AI data analysis → and AI task automation implementation, all at once. This class is optimized for planners, marketers, managers, founders, and others who wish to acquire business productivity and performance enhancement strategies directly applicable to their work.

[How Smart People Think and Question] – Author's simplified lecture! Generative AI era, good questions over answers.

Have you ever actually run a service you built? Deployment and observability, external integrations and queues, files and extensions, and operating AI features. This is Part 2, where you learn what else needs to be decided to keep an AI-built backend serving real users continuously.

You will systematically learn the principles and practical applications of the latest generative AI technologies, from ChatGPT to image and video generation AI and no-code tools. Through seven core topics, including prompt engineering, Copilot utilization, and AI extensions, you can maximize work efficiency and develop creative problem-solving skills.

Learn a practical web production workflow that can be applied immediately in the field—from homepages, landing pages, and interactive websites to AI Agent-based CMS—using AI image/video generation tools and Vibe Coding. This course is not just about creating a single screen with AI. It covers the process of generating images and videos using the latest AI tools such as OpenAI Images 2.0, Kling, and Higgsfield, and utilizing them as hero images, background videos, transition videos, and interaction assets for actual websites. Based on Next.js, you will step-by-step implement cinematic landing pages, game-like interactive homepages, inquiry pages, and blog content pages, mastering structures and workflows that professionals can refer to immediately when creating homepages or landing pages. In particular, this course does not stick solely to traditional admin-page-style CMS. You will implement an AI Agent-based CMS structure where AI Agents like Codex and Claude Code organize content and image assets based on predefined skills and upload rules, then upload them to Neon DB and Vercel Blob Store. In other words, instead of an administrator manually entering data into forms one by one, you will experience an operational method where you provide blog topics, reference materials, and image assets, and the AI Agent organizes the content structure and uploads it in a consistent format. Later, this structure can be further enhanced into traditional admin pages, approval workflows, or automation pipelines as needed. Furthermore, it doesn't end with production; we will prepare elements necessary for actual operation, including Vercel deployment, GoDaddy domain connection, SEO meta tags, favicons, OG images, sitemaps, and robots.txt settings. The course also covers the process of registering sites and verifying ownership on Google Search Console, Bing Webmaster Tools, and Naver Search Advisor, as well as setting up Google Analytics and Google Tag Manager to track visitor data. By the end of the course, you will be able to independently reproduce a professional web production workflow that spans AI-driven planning, image/video asset creation, Next.js implementation, SEO optimization, deployment, search engine registration, analytics tool setup, and AI Agent-based content operation.

Simple tutorials alone make it difficult to apply in practice. I will clearly pass on my professional know-how, covering complex state management and multi-agent design methods.