LangChain

LangChain — Course #405 · Hottest GenAI Skill

Build Production

LLM Applications 🔗

LangChain is the #1 framework for building LLM-powered applications. Create RAG systems, AI chatbots, autonomous agents, and document Q&A tools. Master LangChain with OpenAI, Anthropic, vector databases, and production deployment — the hottest skill set in Generative AI.

LangChain RAG Architecture OpenAI / Anthropic Prompt Templates Chains & Agents Vector DBs — FAISS/ChromaDB LangGraph Memory & State FastAPI Deployment LLM Fine-Tuning

Enroll Now Brochure

LangChain — RAG Pipeline

from langchain.chains import RetrievalQA
from langchain.vectorstores import Chroma
from langchain.chat_models import ChatOpenAI
# Build RAG pipeline over your documents
vectorstore = Chroma.from_documents(
  docs, embedding=OpenAIEmbeddings())
qa_chain = RetrievalQA.from_chain_type(
  llm=ChatOpenAI(model="gpt-4o"),
  retriever=vectorstore.as_retriever())

20K+

Jobs

GenAI demand

42 hrs

Duration

Video content

Modules

Full syllabus

Cert

Included

Industry recognised

24/7

Support

Expert guidance

LangChain

LangGraph

LangSmith

OpenAI GPT-4

Anthropic Claude

Google Gemini

RAG

FAISS

ChromaDB

Pinecone

Weaviate

Prompt Templates

Agents & Tools

Memory

LLM Fine-Tuning

FastAPI

Streamlit

Hugging Face

Ollama (Local LLMs)

Docker

LangChain

LangGraph

LangSmith

OpenAI GPT-4

Anthropic Claude

Google Gemini

RAG

FAISS

ChromaDB

Pinecone

Weaviate

Prompt Templates

Agents & Tools

Memory

LLM Fine-Tuning

FastAPI

Streamlit

Hugging Face

Ollama (Local LLMs)

Docker

What You Master

The Complete LangChain Skill Set

From LLM integration and RAG pipelines to autonomous agents, memory, and production deployment.

🔗

RAG Systems & LLM Applications

Build Retrieval-Augmented Generation (RAG) systems that let LLMs answer questions from your own documents, databases, and APIs. Create chains and pipelines that connect OpenAI, Anthropic Claude, or local LLMs to vector databases (FAISS, ChromaDB, Pinecone) for intelligent, accurate, context-aware responses.

RAG Pipeline Vector DBs Embeddings FAISS/Chroma Prompt Templates Chains Streaming Semantic Search

🤖

Autonomous AI Agents

Build agents that reason, plan, and use tools — web search, Python REPL, APIs, and databases — to complete complex tasks autonomously.

🧠

Memory & State

Conversation buffer memory, summary memory, entity memory — build chatbots that remember context across sessions.

🚀

Production Deployment

Deploy LangChain apps with FastAPI, Streamlit, and LangServe. Monitor with LangSmith. Scale on AWS, GCP, or Hugging Face Spaces.

FastAPI LangSmith LangGraph

Syllabus

Course Curriculum

8 hands-on modules — from LLM basics to production-ready AI applications with RAG and agents.

What is LangChain & why it matters for LLM apps
Core components — LLMs, Chains, Agents, Memory, Retrievers
Setting up environment — Python, OpenAI API, LangChain install
Overview of LLM providers — OpenAI, Anthropic, Google, Cohere

Introduction to Large Language Models (LLMs)
Connecting to OpenAI GPT-4, Anthropic Claude, Google Gemini
Prompt engineering basics — zero-shot, few-shot, chain-of-thought
Temperature, max tokens, and model parameter tuning
Working with local LLMs via Ollama

Understanding and creating PromptTemplates
Dynamic prompts with input variables
LLMChain — the basic building block
Sequential Chains — multi-step reasoning pipelines
Building a chatbot using LLMChain

Why memory matters in conversational AI
ConversationBufferMemory — full history
ConversationSummaryMemory — compressed context
ConversationEntityMemory — track entities
Building a stateful chatbot with persistent memory

What are LangChain agents & how they reason (ReAct)
Built-in tools — Python REPL, Search, Wikipedia, Calculator
Creating custom tools and tool wrappers
Building an autonomous AI assistant
LangGraph for multi-agent workflows

Introduction to Retrieval-Augmented Generation (RAG)
Text splitting and document loading
Embeddings — OpenAI, Sentence Transformers, HuggingFace
Vector stores — FAISS, ChromaDB, Pinecone, Weaviate
Building a document Q&A system with RAG

Exposing LangChain apps via FastAPI & REST APIs
Building chat UI with Streamlit
LangServe for production-ready app serving
Deploying on AWS Lambda, GCP, Hugging Face Spaces
LangSmith for observability & debugging

Streaming LLM responses for real-time UX
Multimodal AI — combining text, images, and audio
Fine-tuning custom models and integrating with LangChain
LangGraph for complex state-machine workflows
Capstone — Build a full RAG chatbot application

Career Outcomes

LangChain / GenAI Careers in 2026

Generative AI developers are the most-sought professionals in 2026. Every startup, enterprise, and tech giant is building LLM-powered products — and they need developers who know LangChain, RAG, and agent architectures.

₹15–50L

India Avg LPA

$130K–$250K

USA Avg

20K+

Active GenAI Jobs

Hottest Skill

2026 Market

LLM / AI Application Developer

Build production AI apps — chatbots, document QA, RAG systems — using LangChain and LLM APIs

₹15–40 LPA · $130K–$200K USA

RAG Engineer

Design and implement retrieval-augmented generation systems for enterprise knowledge management

₹18–45 LPA · $140K–$215K USA

AI Automation Engineer

Build autonomous AI agents that automate complex business workflows using LangGraph and tools

₹20–50 LPA · $145K–$230K USA

GenAI Solutions Architect

Design end-to-end generative AI product architectures for enterprises building on LLM platforms

₹25–70 LPA · $160K–$280K USA

Build Production

LLM Applications 🔗

The Complete LangChain Skill Set

Course Curriculum

LangChain / GenAI Careers in 2026

Ready to Build with LangChain?