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In this course, you will learn how to design, build, and evaluate advanced RAG systems using the LangChain framework. You will master LCEL, Runnables, advanced retrieval techniques, chunking strategies, cross-encoder reranking, agent-based RAG, tool calling, SQL integration, and safety techniques using NeMo Guardrails. You will also learn how to trace, debug, and deploy a full-stack AI chatbot with LangFuse, React, FastAPI, and Docker.

antoinevolver2047

Advanced LangChain Techniques: Mastering RAG Applications- (2026)

LangChain

langchain

Markus Adam

입문

초급

중급이상

대규모 언어 모델

랭체인

검색증강생성

AI 에이전트

AI Agent

Before we start

LangChain Expression Language(LCEL): A Deep Dive

Indexing API

RAGAS - Framework for Evaluating RAG Performance

Chunking Techniques

Embedding Models

Improving queries for better retrieval

Parent Document Retriever - Two-Stage-Retrieval

Agentic RAG

Retrieval - Postprocessing Documents

Routing

NeMo Guardrials

LangFuse

Tool Calling