Search topics…
Tutorials
100 Days of ML 100 Days of DL 100 Days of NLP MCP Trilogy FastAPI for ML PyTorch Deep Learning LangChain & GenAI LangGraph Agents Code Lab (LeetCode AI) Syllabus Path
Explore
All Tutorials Courses Free Resources 1-on-1 Mentoring Corporate Training AI for Beginners Generative AI Course Corporate AI Coaching
June 6 Offline Event →
CampusX · GenAI Roadmap

Generative AI with LangChain Tutorial — Master LLM App Development

Free LangChain Generative AI tutorial — prompts, chains, RAG, vector stores, agents, memory, and production LLM applications. Hindi & English. Free forever.

A comprehensive, step-by-step tutorial series on LangChain — from Generative AI foundations and LCEL to prompt engineering, document loaders, embeddings, RAG, and custom AI agents.

8 Core Modules
16 Parts
100+ Code Examples
∞ Free Forever
Curriculum Overview

8 Modules to AI Engineering

Build real-world LLM applications. Click any module card below to access the detailed notes, code blocks, and architectures.

Module 1
Foundations

Generative AI Foundations

Start with the core theory: what Generative AI is, how Foundation Models work, and the Builder vs User perspectives.

  • What is Generative AI? Core mechanisms
  • Success metrics & industry impact of GenAI
  • Foundation Models: GPT, Claude, Gemini, Llama
  • The Builder vs User side of AI
  • Three ways to improve LLM outputs (Prompt, RAG, FT)
  • Career paths in Generative AI
Start Module 1 →
Module 2
Fundamentals

LangChain Fundamentals

Learn why LangChain is the industry standard framework and master the Models API (LLMs vs Chat Models) and LCEL syntax.

  • What is LangChain? Modular framework architecture
  • Holistic capabilities & applications matrix
  • Models API: LLMs (text) vs Chat Models (messages)
  • Embeddings API & converting text to dense vectors
  • Introduction to LCEL (LangChain Expression Language)
  • Installation, environment setup, and API keys
Start Module 2 →
Module 3
Prompts & Parsers

Prompts & Output Parsing

Master structuring inputs with templates and converting unstructured text outputs into structured Python objects.

  • PromptTemplate & dynamic variable interpolation
  • FewShotPromptTemplate for in-context learning
  • ChatPromptTemplate & role-based messaging
  • MessagesPlaceholder for conversation memory
  • StrOutputParser, JsonOutputParser, and Pydantic
  • Practical parsing patterns and translation chains
Start Module 3 →
Module 4
Chains & LCEL

Chains & LCEL Deep Dive

Go beyond single prompts to compose complex multi-step workflows using modern LangChain Expression Language (LCEL).

  • Legacy LLMChain vs modern LCEL pipes (|)
  • Sequential chains (passing outputs to inputs)
  • Router chains for dynamic logical paths
  • RunnableParallel, RunnableLambda, RunnablePassthrough
  • Chain debugging and LangSmith execution tracing
  • Real-world pipelines: summarization & translation
Start Module 4 →
Module 5
Loaders & Splitters

Document Loaders & Text Splitting

Load external files and split them into semantic chunks to feed into LLM prompts without exceeding token limits.

  • TextLoader, PyPDFLoader, CSVLoader, WebBaseLoader
  • DirectoryLoader for loading folder structures
  • Memory management: load() vs lazy_load()
  • Character vs RecursiveCharacterTextSplitter
  • TokenTextSplitter & SemanticChunker
  • Best practices for choosing chunk sizes & overlap
Start Module 5 →
Module 6
Embeddings & Vectors

Embeddings & Vector Stores

Convert text documents into vector spaces and save them in high-performance databases for semantic search.

  • Text Embeddings: OpenAI vs local HuggingFace models
  • Cosine similarity math & similarity search
  • FAISS vector database for local storage
  • Chroma DB persistent vector stores
  • Pinecone managed vector DBs
  • VectorStore operations and Retriever interfaces
Start Module 6 →
Module 7
RAG Systems

Retrieval-Augmented Generation (RAG)

Build question-answering systems over private documents, handling memory, context retrieval, and reranking.

  • RAG Architecture: Indexing and Retrieval flows
  • Building complete pipelines with Scikit/FAISS
  • RetrievalQA Chain types (stuff, map_reduce, refine)
  • ConversationalRetrievalChain with chat history
  • Advanced retrieval: Multi-Query, Parent Document, HyDE
  • Reranking with CrossEncoders & RAGAS evaluation
Start Module 7 →
Module 8
Agents & Memory

AI Agents & Memory

Create autonomous agents that use reasoning loops (ReAct), select external tools, and keep dynamic long-term memories.

  • ReAct reasoning loops (Thought → Action → Observation)
  • Defining custom tools with @tool decorators
  • Built-in tools: search, calculations, python REPL
  • Modern create_react_agent() & AgentExecutor
  • Memory types: Buffer, Window, Summary, VectorStore
  • Multi-agent coordination using LangGraph
Start Module 8 →

LangChain Syllabus Quick Nav

GenAI Foundations Careers & Improvement LangChain Intro Models & LCEL Prompt Templates Output Parsers Chains & LCEL Advanced Chains Document Loaders Text Splitters Embeddings Vector Stores RAG Pipeline RAG Evaluation Agents & Tools Memory & LangSmith

Ready to Build GenAI Apps?

Start with Module 1 — learn the foundational mechanics of Generative AI and transformers before diving into the code.

Start Module 1 → 100 Days of ML

Frequently Asked Questions

What is RAG and how does LangChain implement it?

RAG (Retrieval-Augmented Generation) is a technique where an LLM retrieves relevant documents from a vector database before generating a response. LangChain implements RAG through document loaders, text splitters, vector stores (like FAISS and Chroma), and retrieval chains. Our free LangChain tutorial covers RAG from scratch with full code examples.

LangChain vs LlamaIndex — which is better?

Both LangChain and LlamaIndex are popular frameworks for building LLM apps. LangChain is more versatile for building chains, agents, and RAG pipelines. LlamaIndex specializes in document indexing and retrieval. For most beginners building GenAI apps in India, LangChain is the better starting point — and GenAIWallah's free LangChain course covers it end-to-end.

How to build an AI agent with LangChain?

To build an AI agent with LangChain, you need to define tools, create a prompt with agent instructions, initialize a LangChain agent (like ReAct or OpenAI Functions agent), and run it with an LLM. Our free LangChain agents module covers this step-by-step with Python code in Hindi and English.

Is this LangChain course free?

Yes. GenAIWallah's LangChain course is completely free — no signup, no paywall. It covers LangChain fundamentals, prompts, chains, RAG, vector stores, agents, and production LLM applications. All content is available in Hindi and English.