Generative Ai With Ai Agents & Mcp For Developers

Master Generative AI, Model Context Protocol (MCP), and build cutting-edge AI Agent Systems with Python & LLMs

What you'll learn

Understand the fundamentals of Generative AI and Large Language Models (LLMs)

Design and build scalable Generative AI applications using Advanced Gen AI Application Architecture

Master Retrieval-Augmented Generation (RAG) techniques for smarter applications

Explore and leverage orchestration frameworks like LangChain and LlamaIndex

Gain hands-on experience with LangChain Expression Language (LCEL) and its Ecosystem

Develop strong Prompt Engineering skills to optimize LLM outputs

Build end-to-end Gen AI applications across multiple complexity levels (Beginner to Professional)

Implement AI Agent and Multi-Agent systems for advanced automation

Integrate Multimodal data (text, image, etc.) into Generative AI applications

Learn LLMOps (Large Language Model Operations) for efficient deployment and management

Deploy Generative AI applications to production using CI/CD pipelines

Understand and implement Model Context Protocol (MCP) for context-aware applications

Fine-tune Large Language Models (LLMs) to fit custom project needs

Work on real-world Generative AI projects to solidify practical knowledge

Requirements

Basic understanding of Python programming

Familiarity with fundamental concepts of machine learning (helpful but not mandatory)

No prior experience with Generative AI or LLMs required

Curiosity and willingness to learn cutting-edge AI technologies

Description

This hands-on course teaches you how to build professional level Generative AI Application, intelligent, autonomous AI Agents using MCP (Model Context Protocol) and modern LLM frameworks.Whether you’re an AI beginner or an experienced developer, this course will take you step-by-step through the tools, strategies, and architectures that power modern GenAI applications.What You’ll Learn:- Introduction to Generative AI and its role in modern development- Introduction to Large Language Models (LLMs) and how they power intelligent applications- Generative AI Architecture Basics – understand the core components of a Gen AI application- Advanced Gen AI Application Architecture for scalable and modular systems- How to apply the Retrieval-Augmented Generation (RAG) technique for enhanced responses- Choosing the Right Orchestration Framework for building LLM-powered apps- LangChain – A modern framework for LLM orchestration- LangChain Expression Language (LCEL) – Build AI flows with clean, declarative syntax- Deep dive into the LangChain Ecosystem for agents, tools, memory, and chains- Mastering Prompt Engineering – Learn to craft optimal prompts for LLMs- Level 1 Gen AI Applications – Basic AI-powered tools and assistants- LlamaIndex – An alternative to LangChain for RAG and LLM app orchestration- LLMOps (Large Language Model Operations) – Manage and monitor LLM Apps- Level 2 Gen AI Applications – Build intermediate systems with memory, tools, and retrieval- Develop Multimodal Gen AI Applications (text, image, audio integration)- Build and deploy AI Agents & Multi-Agent Systems using orchestration frameworks- Level 3 (Professional) Gen AI Applications – Real-time, scalable, production-ready systems- CI/CD for Gen AI – Deploy your Gen AI apps with automated pipelines- Understand and implement MCP (Model Context Protocol) - Hands-on Projects – From AI assistants to autonomous agents and RAG-powered apps- Fine-tuning LLMs for domain-specific use cases and better performance

Overview

Section 1: Introduction to the Course

Lecture 1 Introduction to the Course & Content

Section 2: Introduction to Generative AI

Lecture 2 Introduction to Generative AI

Section 3: Introduction to Large Language Models (LLMs)

Lecture 3 Introduction to Large Language Models & its architecture

Lecture 4 In depth intuition of Transformer Architecture

Lecture 5 How LLM is trained?

Section 4: Introduction & Architecture of a Generative AI Application

Lecture 6 Basic Architecture Overview for Gen AI Applications

Lecture 7 Advanced Gen AI Application Architectures

Lecture 8 Multi-Level Architecture Exploration (Level 1, Level 2, Level 3)

Lecture 9 Preview of a Professional Gen AI Application

Section 5: LLMs & Frameworks for Generative AI

Lecture 10 Selecting the Right Foundation LLMs

Lecture 11 Comprehensive Tool Stack for Gen AI Applications

Lecture 12 Orchestration Frameworks for Scalable Solutions

Section 6: Retrieval-Augmented Generation (RAG) Technique

Lecture 13 Introduction to RAG and Key Concepts

Lecture 14 Important Concepts of RAG

Lecture 15 Core Components of RAG

Lecture 16 Addressing RAG Implementation Challenges

Section 7: Choosing Orchestration Frameworks for Application Development

Lecture 17 Choosing Orchestration Frameworks for Application Development

Section 8: LangChain - A Modern Orchestration Framework

Lecture 18 Overview of LangChain, Evolution, and Learning Path

Lecture 19 Connecting with Leading LLMs

Lecture 20 Prompt Templates for Integrating Logic into LLM Interactions

Lecture 21 Chains for Sequencing Instructions

Lecture 22 Output Parsers for Response Formatting

Lecture 23 Working with Custom Data (Data Loaders) & RAG Basic Concepts

Lecture 24 Different RAG Components

Lecture 25 Basic RAG Implementation with LCEL

Lecture 26 Memory Management in LangChain: Temporary and Permanent Memory

Section 9: LangChain Expression Language (LCEL)

Lecture 27 Introduction to Langchain Expression Language (LCEL) - Chains and Runnables

Lecture 28 Built-in Runnables in LCEL

Lecture 29 Built-in Functions in runnables

Lecture 30 Combining LCEL Chains

Lecture 31 RAG demo with LCEL

Section 10: LangChain Ecosystem

Lecture 32 Comprehensive Overview of the LangChain Ecosystem

Lecture 33 LangServe Demo

Lecture 34 LangGraph Demo

Lecture 35 LangSmith Demo

Section 11: Mastering Prompt Engineering

Lecture 36 Prompt Engineering

Section 12: Level 1 Application Development

Lecture 37 Introduction to Level 1 Application

Lecture 38 Advanced Chatbot with Memory

Lecture 39 Key Data Extraction

Lecture 40 Sentiment Analysis Tool

Lecture 41 SQL-based Question Answering Application

Lecture 42 PDF-based Question Answering

Lecture 43 Basic Retriever Applications

Lecture 44 RAG Application

Section 13: Level 2 Application Development

Lecture 45 Introduction to Level 2 Application

Lecture 46 Application for Converting Slang to Formal English

Lecture 47 Blog Post Generation Application

Lecture 48 Text Summarization with Split

Lecture 49 Text Summarization Tools

Lecture 50 Key Data Extraction from Product Reviews

Lecture 51 Interview Questions Creator Application

Lecture 52 Medical Chatbot Project

Section 14: LlamaIndex - An Alternative of LangChain

Lecture 53 Introduction to LlamaIndex

Lecture 54 In-depth Exploration of LlamaIndex

Section 15: Multimodal Gen AI Applications

Lecture 55 Overview of Multimodal LLM Applications

Lecture 56 Steps to implement Multimodal LLM Applications

Lecture 57 Building Multimodal LLM Applications with LangChain & GPT 4o Vision

Section 16: Level 3 (Professional) Application Development

Lecture 58 Introduction to Level 3 Application

Lecture 59 Project 1: Advanced RAG-Based Knowledge Management System

Lecture 60 Project 2: Medical Diagnostics Support Application

Section 17: Deploying Gen AI Applications with CI/CD for Production

Lecture 61 Complete CICD Deployment

Section 18: LLMOps - Large Language Model Operations

Lecture 62 What is LLMOps?

Lecture 63 Why LLMOps is Different from Traditional MLOps

Lecture 64 The Evolution from MLOps to LLMOps

Lecture 65 FastAPI for LLM Inference

Lecture 66 Setup MLflow on AWS for LLMOps

Lecture 67 Training Models with MLflow A Hands-On Guide

Lecture 68 MLflow for Model Inference

Lecture 69 Dockerizing LLM Inference Services

Lecture 70 LLM Evaluation With MLflow And Dagshub

Lecture 71 Why we need LLMOps Platform

Lecture 72 Generative AI with Google Cloud (Vertex AI) a LLMOps Platform

Lecture 73 Vertex AI Hands-On on Google Cloud

Lecture 74 Vertex AI Local Setup - Run Gemini on Local Machine

Lecture 75 RAG on Vertex AI with Vector Search and Gemini Pro

Lecture 76 LLM powered application on Vertex AI

Lecture 77 Fine tuning Foundation Model VertexAI

Lecture 78 Introduction to AWS Bedrock

Lecture 79 Hands-on AWS Bedrock

Lecture 80 End to End Project using AWS Bedrock

Section 19: Fine-Tuning Large Language Models using PEFT

Lecture 81 RAG Vs Fine-tuning

Lecture 82 What is Fine Tuning

Lecture 83 Fine-Tuning Meta Llama 2 on Custom Data

Section 20: AI Agents

Lecture 84 Introduction to AI Agents and Agentic Behaviors

Lecture 85 Multi-Agent Development with CrewAI

Lecture 86 Implementation of AI Agents using LangChain

Lecture 87 Implementation of AI Agents using LangGraph

Lecture 88 Implementation of AI Agents using Phidata

Lecture 89 Implementation of AI Agents using LangFlow

Lecture 90 Video Summarizer Agent

Lecture 91 Agentic RAG using CrewAI

Section 21: Model Context Protocol (MCP)

Lecture 92 Introduction to MCP

Lecture 93 Setup MCP Server on Cursor

Lecture 94 Implement AI Agent with MCP using MCP-USE

Developers and software engineers interested in building Generative AI applications,Data scientists and machine learning engineers looking to integrate LLMs into real-world projects,AI enthusiasts eager to explore cutting-edge concepts like AI Agents, MCP, RAG, and LLMOps,Students and researchers who want practical experience in developing AI-powered applications,nyone curious about building end-to-end, production-ready Generative AI systems, from beginner to advanced levels