The most comprehensive GenAI course — LLMs, prompt engineering, RAG, AI agents, MCP, LangChain, LangGraph, Google ADK, and n8n. No fluff. Real code. Real systems.
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph
# Build a multi-agent system
model = ChatOpenAI(model="gpt-4o")
graph = StateGraph(AgentState)
graph.add_node("researcher", researcher)
graph.add_node("writer", writer)
graph.add_edge("researcher", "writer")
app = graph.compile()
# ✓ Multi-agent pipeline readyReal projects, not toy demos. Every module leads to something you can ship.
Build a full retrieval-augmented generation system with vector stores, embeddings, and reranking.
Design supervised agent teams with LangGraph — state machines, routing, and human-in-the-loop.
Create production n8n workflows that connect AI models with real-world APIs and triggers.
Build Model Context Protocol servers that let AI agents interact with external tools and data.
16 modules · 182+ lessons · 80+ hours of content
You write code but haven't touched LLMs yet. This takes you from zero to building real AI systems.
You need production patterns — RAG, agents, tool use, MCP — not toy demos.
Understand what's possible, what's practical, and where AI actually delivers value.
“The Indian analogies hit different. Finally understood transformers because of the 'wedding table' strategy. This is how AI should be taught.”
“Went from calling APIs to building AI RAG pipelines in a week. The LangChain module alone is worth it.”
“The MCP and A2A lessons are incredibly current. My team is implementing these right now.”