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Advanced LangChain Techniques: Mastering RAG Applications

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.

2 learners are taking this course

  • Markus Lang
langchain
LangChain
LLM
RAG
AI Agent

What you will gain after the course

  • The ability to build advanced RAG pipelines using LangChain, including retrieval optimization, reranking, routing, and tool integration.

  • The skills to evaluate and improve RAG systems using RAGAS and AI-generated test sets.

  • Hands-on experience building a full-stack AI application (React + FastAPI + Docker) with production-ready features.

  • The knowledge to integrate NeMo Guardrails for safe, reliable, and controlled AI behavior.

  • Practical expertise with LCEL, Runnables, and LangChain’s modern pipeline structure.

Build Next-Level RAG Applications with LangChain: LCEL, Advanced Retrieval, RAG Evaluation, Guardrails & Full-Stack AI Deployment

A complete, hands-on guide to mastering advanced Retrieval-Augmented Generation (RAG) using the LangChain framework.
Students will learn LCEL, advanced retrieval strategies, RAG evaluation with RAGAS, NeMo Guardrails, tool calling, agents, routing, and full-stack AI deployment—skills used in modern AI engineering, data science, and AI product development.

Recommended For

Who This Course Is For (1)

This course is designed for learners who already understand the basics of LangChain but feel stuck when building real, production-grade RAG systems.
If you are unsure how to evaluate RAG pipelines, optimize retrieval, implement safety guardrails, or build full-stack AI applications, this course will answer those concerns.

Who This Course Is For (2)

Engineers who struggle with query formulation, chunking strategies, reranking, or routing will benefit from the step-by-step deep dives.
If you're a developer who wants to go beyond tutorials and truly understand how modern RAG systems work under the hood, this course is for you.

Who This Course Is For (3)

Perfect for Software Engineers, AI Developers, and Data Scientists who already have intermediate Python skills and want to bring their RAG applications to the next level—whether for professional projects, enterprise AI systems, or personal AI tools.

After Taking This Course

  • By the end of this course, students will be able to:

    • Use LCEL and Runnables to build robust, maintainable LangChain pipelines

    • Design advanced RAG systems with techniques like MultiQuery Retrieval, HyDE, parent-document retrieval, reranking, routing, agents, and cross-encoder models

    • Build and evaluate AI systems with RAGAS (including AI-augmented test sets)

    • Implement custom document stores, advanced chunking strategies, and indexing pipelines

    • Apply safety and reliability using NeMo Guardrails integrated into LangChain

    • Prevent SQL injection and build SQL-LLM hybrid systems

    • Implement tool calling, chat history, and chain tracing with LangFuse

    • Build and deploy a full-stack RAG chatbot (React + FastAPI + Docker)

    • Confidently build production-ready RAG applications from scratch

    This course helps students turn theoretical knowledge into practical, portfolio-ready projects suitable for interviews, freelancing, and enterprise development.

Frequently Asked Questions

Q. Why should I learn Advanced RAG with LangChain?

Because RAG is becoming the backbone of real AI applications—chatbots, assistants, enterprise search, automation, and knowledge management.
Basic RAG is no longer enough. Companies now need advanced retrieval, evaluation, safety guardrails, and full-stack integration. This course gives you exactly that.

Q. What can I do after learning these topics?

You’ll be able to build:

  • Production-grade RAG systems

  • AI knowledge assistants

  • Search-augmented chatbots

  • Enterprise-ready LLM tools with safety railguards

  • Full-stack AI apps connected to APIs, SQL databases, and vectorstores
    These are immediately useful for jobs in AI engineering, backend engineering, and data science.

Q. How in-depth is the course content?

This is an intermediate → advanced course.
We go beyond surface-level explanations and dive into LCEL internals, retrieval optimization, evaluation pipelines, agentic RAG, custom docstores, Guardrails integration, and full-stack deployment.

Q. Is there anything I should prepare before taking this course?

Yes—intermediate Python, basic LangChain knowledge, and comfort using the terminal and Docker.
A GPU is not required; CPU is enough for all demonstrations.

Q. Does the course include a full project?

Yes. You will build a complete RAG chatbot application from scratch using React, FastAPI, LangChain, and Docker.

Before You Enroll

Before You Enroll

Practice Environment

Operating Systems Supported

  • Windows

  • macOS

  • Linux

Required Tools

  • Python (virtual environment)

  • Docker

  • Terminal (Bash, PowerShell, or zsh)

  • VS Code or any editor

  • Access to OpenAI or alternative LLM APIs (optional for certain modules)

Recommended PC Specs

  • CPU: Quad-core

  • RAM: 8GB minimum, 16GB recommended

  • Storage: 10GB free

  • GPU: NOT required

Learning Materials Provided

  • Source code (full repository)

  • Helper scripts for data ingestion, cleaning, and inspection

  • Full-stack RAG chatbot project

  • PPT-style explanations inside the videos

  • 3.5 hours of on-demand video

  • Additional articles

  • Dockerized environment for easy setup

All resources are lightweight and designed for easy reproduction.

Prerequisites & Notices

  • Prior knowledge: Intermediate Python, LangChain basics, and familiarity with APIs.

  • Video quality: Full HD.

  • Recommended study method: Follow along by coding during the lessons for best retention.

  • Support: Students can ask questions via the platform’s Q&A section.

  • Updates: The course will be updated as LangChain, RAGAS, and Guardrails evolve.

Copyright Notice:
All course materials—including code, scripts, videos, and assets—are for personal use only. Redistribution or commercial reuse is prohibited.


Recommended for
these people

Who is this course right for?

  • Developers or data scientists who already understand the basics of LangChain but struggle to build high-quality, production-ready RAG applications.

  • Engineers who are frustrated with poor retrieval accuracy, bad chunking, or unreliable AI behavior and need a structured way to improve their pipelines.

  • Anyone working on an AI project who needs to evaluate RAG systems, integrate guardrails, or deploy a full-stack RAG application.

Need to know before starting?

  • Yes. Learners should have intermediate Python knowledge and basic familiarity with LangChain concepts. Understanding data types, functions, OOP, and working with APIs will help follow the examples more smoothly. Basic knowledge of terminal commands and Docker is recommended.

Hello
This is

Hello, I'm Markus, a software developer specializing in Artificial Intelligence and Python. I work in the finance industry and have extensive experience developing LLM applications with LangChain and successfully deploying them into production.

I am passionate about teaching and strive to make complex topics approachable and practical for my students, focusing on providing clear, hands-on learning experiences.

I’m excited to share my knowledge with you and help you grow your skills.

I look forward to welcoming you to my courses and being part of your learning journey!

Curriculum

All

36 lectures ∙ (3hr 28min)

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