It was easy to understand because you explained it so well.

kansin885601

This course covers the entire process of designing and implementing generative AI services centered on LangChain 1.0 and LangGraph through step-by-step hands-on practice.

Beyond simple LLM calls, you'll directly implement operational AI system architectures that include agent-based architecture, state management, memory, streaming, middleware, and Human-in-the-Loop.

Through hands-on practice with document/PDF/web data-based RAG systems, SQL Agent (Chinook DB), tool-calling-based Agents, Supervisor pattern multi-agents, and state machine-based workflows using LangGraph Graph API, you'll build reusable agent pipelines that can be immediately applied in real-world scenarios.

Additionally, through structured output (Pydantic-based), agent middleware (Summarization, HITL, Retry, PII protection), and token/step-by-step streaming, you'll complete generative AI applications with the stability, scalability, and controllability required in actual services.

👉 For those who want to accurately understand the internal structure and execution flow of LangChain/LangGraph
👉 For those who want to implement RAG·Agent as a real service structure, not just a "demo"
👉 This is the optimal course for those who need a realistic practical roadmap covering state-based agents, SQL·document automation, and multi-agent orchestration.

trimurti

Building Generative AI Services with LangChain version 1.0

실습 중심

ai in practice

AI 활용법

YoungJea Oh

입문

초급

중급이상

챗지피티

ChatGPT

prompt engineering

랭체인

LangChain

Generative AI

실습 환경 설정

API Key 생성 및 저장

LangChain 개요

Agent

검색 증강 생성 (RAG, Retrieval Augmented Generation)

Agent 응용

LangGraph