The Complete LangChain & RAG Developer Course 2026

The Complete LangChain & RAG Developer Course 2026
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch

Build Production-Ready AI Applications with LangChain, OpenAI, FAISS & ChromaDB
Master Retrieval-Augmented Generation (RAG) and Build Real AI Systems from Scratch
Are you ready to master one of the
most in-demand skills in Generative AI engineering
?
Welcome to
The Complete LangChain & RAG Developer Course 2026
– a hands-on, beginner-friendly course designed to help you build powerful AI applications using
LangChain, OpenAI, FAISS, ChromaDB,
and
Retrieval-Augmented Generation (RAG)
.
In this course, you’ll learn how modern AI systems like ChatGPT-style assistants retrieve real-time knowledge from PDFs, documents, databases, and custom data sources to generate
accurate, context-aware responses
.
This is not just theory.
You will build a
complete end-to-end RAG application
using real-world workflows and industry-standard tools used by modern AI engineers.
What You’ll Learn
By the end of this course, you will be able to:
•
Understand how
Retrieval-Augmented Generation (RAG)

works
•
Build AI applications powered by
LangChain
•
Process
PDFs, CSVs, and DOCX
files
for AI pipelines
•
Master
text
chunking strategies
for better retrieval accuracy
•
Generate embeddings and perform

semantic similarity search
•
Work with vector databases like

FAISS
and
ChromaDB
•
Build scalable
LangChain
runnable pipelines
•
Create
production-ready AI retrieval systems
•
Use
prompt engineering
for better LLM responses
•
Structure outputs
using
Pydantic
•
Build a complete
Capstone RAG Project
from scratch
Why Learn RAG & LangChain?
Traditional Large Language Models (LLMs) are powerful – but they suffer from:
•
Hallucinations
•
Outdated knowledge
•
No access to private data
•
Limited context windows
Retrieval-Augmented Generation (RAG)
solves these problems by combining:
•
Large Language Models (LLMs)
•
Semantic Search
•
Embeddings
•
Vector Databases
•
Intelligent Retrieval Pipelines
This technology powers:
•
AI Assistants
•
Enterprise Chatbots
•
Knowledge Bases
•
Document Q&A Systems
•
AI Search Engines
•
Customer Support AI
•
Internal Company GPTs
RAG Engineers
and
LangChain Developers
are becoming some of the most sought-after professionals in AI today.
What Makes This Course Different?
Unlike many tutorials that only cover isolated concepts, this course focuses on:
•
Practical implementation
•
Real-world workflows
•
Beginner-friendly explanations
•
Step-by-step coding
•
Industry-standard architecture
•
Production-oriented development
You won’t just learn concepts.
You’ll build
real AI systems
.
Course Curriculum Overview
Learn the fundamentals of
Retrieval-Augmented Generation
and build your first AI-powered application using
LangChain
and
OpenAI
.
Module 2 – Document Loading & Multi-Format Data Ingestion
Teach your AI to process
PDFs, CSV files,
and
DOCX documents
using practical LangChain loaders.
Master chunking strategies that dramatically improve retrieval quality and response accuracy.
Module 4 – Embeddings, Semantic Search & Vector Databases
Understand embeddings, vector search,
FAISS, ChromaDB,
and semantic similarity in depth.
Module 5 – LangChain Runnables & AI Pipeline Composition
Build modular, scalable AI workflows using
LangChain runnables
and chaining techniques.
Module 6 – Capstone Project: Build a Complete End-to-End RAG Application
Bring everything together by building a
production-ready RAG pipeline
from scratch.
You will:
•
Load documents
•
Chunk text intelligently
•
Generate embeddings
•
Build a retriever
•
Create runnable chains
•
Engineer prompts
•
Parse structured outputs
•
Test and validate the final AI system
Tools & Technologies Covered
•
LangChain
•
OpenAI API
•
Python
•
FAISS
•
ChromaDB
•
Embeddings
•
Vector Databases
•
Semantic Search
•
Pydantic
•
Runnable Chains
•
Prompt Engineering
•
Retrieval-Augmented Generation (RAG)
Who This Course Is For
This course is perfect for:
•
Python Developers
•
AI Engineers
•
Machine Learning Enthusiasts
•
LangChain Beginners
•
Generative AI Developers
•
Software Engineers
•
Students entering the AI industry
•
Anyone wanting to build AI-powered applications
Prerequisites
Basic Python knowledge is recommended.
No prior experience with the following is required:
•
LangChain
•
Vector Databases
•
RAG
•
Embeddings
•
Semantic Search
Everything is taught
step-by-step
in a beginner-friendly manner.
If you want to become a modern AI developer and master one of the
most important technologies in Generative AI
, this course is for you.
production-ready RAG applications
with
LangChain, OpenAI, FAISS,
and
ChromaDB

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