Spring AI in Practice: Implementing Chatbots, RAG, and MCP in Spring Services

The ultimate guide for job seekers and current developers new to log management and monitoring! Learn log management methods using the ELK stack, and monitoring using Prometheus and Grafana.

This is a 'Spring Batch' course that can be used immediately in practice, designed for job seekers and working developers who are new to Spring Batch!

The most definitive practical guide for job seekers and junior developers new to CI/CD automation! You will perfectly master everything from building CI/CD pipelines using Jenkins to creating production-level automated deployment environments integrated with AWS infrastructure (S3, ECS).

Through the implementation of a first-come, first-served coupon system, you will solve key real-world backend challenges such as concurrency issues, data consistency, and handling peak traffic. By writing the code yourself, you will identify problems and master essential practical skills, including synchronous/asynchronous processing and queue management. This project-based course is designed to help junior developers and job seekers gain hands-on experience.

Develop and deploy servers using Java, Spring Boot, JPA, MySQL, and AWS. While developing web applications, you will learn all the background knowledge, theories, and various technologies required for server development, as well as the meaning of various options, the history of Spring Boot, and the changes in the latest version.

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.

Have you ever seen a 200 million won chatbot being discarded? Between 70% and 85% of AI projects fail to achieve their expected results. In manufacturing, this number hits even harder. A chatbot that looked perfect in a vendor demo starts confidently giving wrong answers once actual field documents are fed into it. Skilled workers with 30 years of experience turn away, saying, "That thing is useless." Systems built with hundreds of millions of won are quietly abandoned. For over 16 years, I have personally planned and executed MES, Smart Factory, and Manufacturing DX/AX projects at Samsung Electronics, Samsung Display, Hyundai Mobis, and LS ELECTRIC. Now, I am designing and building AI chatbots specifically for manufacturing sites. Through this process, I have constantly pondered, "Why do so many manufacturing chatbots fail?" and I have reached the conclusion that the answer lies not in the technology, but in the planning. This course does not teach code. This is not a course for developers who build chatbots. It is for those who must plan the introduction of a chatbot, evaluate vendors, verify performance, and ultimately decide whether to adopt the system. It is designed for planners, managers, and team leaders in the manufacturing field. Without coding, in just 50 minutes, you will be able to: 1. Understand core AI chatbot technologies (RAG, Knowledge Graph) through simple analogies. 2. Immediately use 5 practical questions to distinguish a vendor's true capabilities. 3. Design a 30-day Proof of Concept (POC) using a single-page checklist. 4. Establish strategies to integrate chatbots without resistance from field workers. Why is this course necessary? - While the AI adoption rate in the Korean manufacturing industry remains at 24%, 81% of companies plan to expand their AI investment. This means that while adoption is low, the will to implement it is high. The problem is a lack of planners who know how to execute. - There are courses on Inflearn regarding RAG and chatbots, but they are all aimed at developers. This is the first AI chatbot planning course specifically for non-developers in the manufacturing industry. 50 minutes can prevent a 200 million won mistake.

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.

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.

Beyond a chatbot that simply generates answers, you will directly implement an autonomous AI Agent that makes its own judgments, calls tools, and performs multi-step tasks. You will learn through step-by-step practice, covering everything from OpenAI Agents SDK's Tool Calling, Handoff, Guardrail, Session, and Tracing to connecting external tools and databases using MCP, as well as LangChain and Deep Agents' Planning, Subagents, Filesystem, and Memory. The ultimate goal is to equip you with the ability to design and implement tool-integrated AI Agents that can be applied to real-world tasks.

Understand Transformer-based LLM architectures and GPU utilization strategies, and gain hands-on experience with the actual serving process using vLLM. This course covers the entire practical workflow, from building AI system pipelines to monitoring and multi-GPU utilization, and is designed for intuitive understanding through diagrams and practice without complex formulas.

AI agents can fail due to various reasons, including data errors, security threats, increasing costs, and system integration issues. This course examines major obstacles and failure cases that occur during the AI agent implementation process and explains practical strategies for design, inspection, and operation to prevent them. Through this, you can understand the risks and limitations of AI agents and establish appropriate implementation directions and stable operational standards for your organization.

This course is a practical-focused learning program for JWT-based token authentication and security enhancement techniques that are essential in modern web service environments. Using Spring Boot 3, Spring Security 6, and React, you'll implement secure login systems and cover comprehensive security practices including XSS·CSRF countermeasures, HTTPS implementation, token reissuance and revocation strategies, and secure secret key management. Additionally, you'll practice real service-level environment configuration through AWS build·deployment and performance optimization using Redis, and the complete project source code provided in the course can be directly applied to real-world work.

This is training on how to use Gemini, which is available in Google Workspace Business editions and above. It may be less useful for individual users with id@gmail.com accounts, and it is not available in Google Workspace Starter. The training explains the features and methods that Google Workspace Business and above users can use to apply Gemini to their company work. We continuously maintain the latest updates. Even after completing the training, check periodically for updates or changes.

A practical course on easily building a super simple bulletin board with Spring Boot and React.js! This course focuses less on theory and more on hands-on practice, covering the web application development process for quick completion.

[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.

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.

Learn how to structure diverse data—including students’ grades, school records, and areas of interest—using the AI-SPARS tool, and leverage an LLM to design everything from major exploration and college/admissions strategies to personalized roadmaps. Rather than simple Q&A, you can immediately apply in practice a systematic AX-style career and college counseling process that progresses from Profile-Interview-Major-College-Admissions Track-Gap-Roadmap-Smart Report-Portfolio.

This video shows effective ways to link internal company data to sLM, and practical solutions for various issues from sLM adoption.

This course consists of a curriculum designed to achieve overwhelming efficiency in various fields such as planning, development, marketing, and job preparation by utilizing 'Perplexity AI,' an AI answer engine that goes beyond the limitations of traditional search engines.

You can quickly learn about virtual threads supported from spring boot 3.2.0 (+ JDK 21) and get hints on performance improvement in just 1 hour for free.

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.

Using ChatGPT is completely different from developing an actual generative AI service. This is because you must master everything from API calls to conversation state management, structured outputs, function calling, document-based RAG, MCP integration, and AI agent implementation. Based on extensive experience in data analysis and AI instruction, this course explains complex concepts through Python-centered practice. You will learn step-by-step—from the principles of generative AI to the OpenAI Responses API, multimodal features, File Search-based RAG, external tool integration, and the Agents SDK—to complete a fully functional AI application.