graphRAG - Knowledge Graph-based RAG System Implemented with Neo4J (feat. LangChain)

An intelligent Retrieval-Augmented Generation (RAG) AI agent built with LangGraph! A hands-on tutorial covering everything from theory to practice, easy for beginners to follow.

This lecture covers building practical RAG (Retrieval-Augmented Generation) systems while developing an AI customer service chatbot (AICC).

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.

Through this course, you will learn about SQL and databases, SQL syntax, database design for practical use, how to use databases through programming, and real-world applications of data analysis.

[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.

This course is designed to provide step-by-step learning from the basic concepts of database systems to the utilization of Relational Database Management Systems (RDBMS) and SQL. Learners will understand the principles of databases and be able to create systematic data structures through data modeling and design methods. Additionally, they will experience actual data processing by practicing data manipulation and management techniques using SQL. Through this course, students can go beyond simple theoretical learning to build a foundation that can be utilized for both practical work and certification exam preparation by acquiring efficient data management and application skills.

A comprehensive course covering AI-era-proof Silicon Valley internships, 30 big-tech system designs, and hands-on open-source contributions. Using real-world cases from Netflix, Tesla, Meta, Tinder, and other big tech companies, you’ll design and scale 30 practical system designs. Finally, you’ll design your own service from architecture to database to scaling.

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

We examine AI security incidents and the role of red teams, then set up an LLM practice environment in our own AWS account. From capturing the initial response to checking usage and cleaning up resources, we record everything in the Mission 0 checklist. The prompt injection diagnosis exercise continues in Part 4.

Master intermediate-to-advanced SQL and build solid SQL data analysis skills by solving 101 real-world data analysis problems together.

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.

Learn NoSQL technology for handling big data, one of the fundamental technologies used in the full-stack and data science fields at modern startups. MongoDB is one of the easiest NoSQL technologies to learn and use quickly. In this course, you will learn the basics of MongoDB in a short time, as well as how to work with and utilize MongoDB using Python.

Connect the diagnostic basis from the previous lecture to the organization’s improvement plan. Review Shadow AI’s data flows and control application requirements, and write a control design document and red-team report containing observed evidence, impacts, limitations, action owners, and retesting criteria.

From SQL basics to practical analysis! Learn powerful SQL skills to solve business problems by extracting, transforming, and analyzing data.

Do you still code by hand these days? We’re in an era when even vibe coding feels like a chore! Become an AI automation expert with n8n—no coding required. A guide to designing automation workflows you can put to work right away Practical, hands-on automation strategies to maximize efficiency. Build intuitive workflows with n8n and experience a revolution in the way you work.

Develop various features using Spring WebFlux. This course will teach you WebFlux's essential concepts and practical implementation.

Before execution, define the scope, rules of engagement, test conditions, and grounds for assessment. Link AI assets and trust boundaries with STRIDE scenarios to create a red team plan and threat model, and distinguish what can be stated based on the results of repeated tests.

🧩 Rather than focusing on complex formulas or theory-centric explanations, the goal is to learn the core concepts of recommendation systems by directly implementing programs. 🛠️ Through a total of 12 diverse and practical examples, we have progressively designed recommendation systems that can be utilized in real-world environments, including content-based recommendation, collaborative filtering, and deep learning recommendation.

By implementing various practical data analysis cases using SQL, you can simultaneously improve your data analysis and SQL utilization skills.

Learn the entire process of running the latest Gemma 4 model directly on your MacBook without the burden of paid API costs. You will master performance maximization techniques using Apple Silicon's Metal API and optimal parameter settings for different VRAM capacities, while gaining the ability to build production-level local AI infrastructure based on FastAPI.