The Complete LangChain & RAG Developer Course 2026

Udemy MOOC / Non-credit USD 199.99
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The Complete LangChain & RAG Developer Course 2026

About this course

Build Production-Ready AI Applications with LangChain, OpenAI, FAISS & ChromaDBMaster Retrieval-Augmented Generation (RAG) and Build Real AI Systems from ScratchAre 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 LearnBy the end of this course, you will be able to:Understand how Retrieval-Augmented Generation (RAG) worksBuild AI applications powered by LangChainProcess PDFs, CSVs, and DOCX files for AI pipelinesMaster text chunking strategies for better retrieval accuracyGenerate embeddings and perform semantic similarity searchWork with vector databases like FAISS and ChromaDBBuild scalable LangChain runnable pipelinesCreate production-ready AI retrieval systemsUse prompt engineering for better LLM responsesStructure outputs using Pydantic

What you'll learn

  • 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

Skills you'll gain

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