GENAI Live Course

This is a Live course of GENAI.
 

Course start Date: 3 Dec 2025
Class Timing: Wed and Fri (9pm) Live
Recording will be available after the class
GenAI course: Javascript

 

 

GenAI Course

Course Title: The Complete Generative AI Engineering Bootcamp: Build & Deploy Autonomous AI Agents

Course Description: This is the definitive course for anyone serious about building the next generation of AI. We will take you on a comprehensive journey from zero to hero, starting with the fundamental concepts of Generative AI and the Transformer architecture. You will then immediately apply this knowledge to build, test, and deploy sophisticated, autonomous AI agents capable of reasoning, planning, and using tools to solve complex problems. Using the entire modern stack, including LangChain, LangGraph, and LangSmith, you will graduate with a portfolio of intelligent agents and the complete skill set of a modern AI Engineer.

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PART I: CORE CONCEPTS & THEORETICAL FOUNDATIONS

Module 1: The New Age of AI: Introduction to Generative AI

• 1.1. The AI Landscape: Differentiating between AI, Machine Learning, Deep Learning, and Generative AI.
• 1.2. What is Generative AI? Understanding the shift from discriminative to generative models.
• 1.3. A High-Level Look at LLMs: What are they, what can they do, and what are their limitations?
• 1.4. The GenAI Ecosystem: Key Players (OpenAI, Google, Meta), Models (GPT, Llama), and the Open-Source Movement.

Module 2: How Language Models "Think": Tokens, Prompts, and Predictions

• 2.1. From Text to Numbers: The Concept of Tokenization.
• 2.2. The Core Interaction: Prompts, Completions, and the Context Window.
• 2.3. The Predictive Engine: An intuitive look at the autoregressive nature of LLMs.
• 2.4. Parametric Knowledge vs. Source Knowledge: Understanding why external data is crucial for building smart agents.

Module 3: Unlocking the Black Box: An Intuition for Deep Learning

• 3.1. The Building Blocks: Artificial Neurons and Neural Networks.
• 3.2. The Learning Process: A clear, non-mathematical explanation of Loss Functions, Gradient Descent, and Backpropagation.
• 3.3. Why "Deep" Learning? The power of multiple layers in learning complex patterns.

Module 4: The Engine of Modern LLMs: The Transformer Architecture

• 4.1. The Core Innovation: The Self-Attention Mechanism.
• 4.2. Positional Encodings: How a model that processes everything at once still understands word order.
• 4.3. Deconstructing the Transformer: A walkthrough of its key components.

Module 5: Representing Meaning: Embeddings & Vector Databases

• 5.1. What are Vector Embeddings? The powerful idea of representing data as points in a high-dimensional space.
• 5.2. The Need for Vector Databases: Why SQL and NoSQL databases are not enough for AI.
• 5.3. Inside the Search Engine: Intuition behind Approximate Nearest Neighbor (ANN) search algorithms.
• 5.4. Hands-On with Vector DBs: Setting up and using databases like Qdrant, Pinecone, or PG Vector.

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PART II: BUILDING APPLICATIONS & AUTONOMOUS AGENTS

Module 6: The Modern AI Stack: Introduction to LangChain

• 6.1. Why LangChain? The philosophy of composing LLM calls into chains and applications.
• 6.2. Core LangChain Components: Models, Prompts, and Output Parsers.
• 6.3. Building Your First LLM Chain: A hands-on "Hello, World!" for GenAI applications.

Module 7: Building the Knowledge Base for Agents: RAG In-Depth

• 7.1. Architecting the Full RAG Pipeline: Ingestion, Chunking, Embedding, Indexing, Retrieval, and Generation.
• 7.2. Practical Implementation: Building a robust "Chat with Your Documents" application.
• 7.3. Laying the Foundation: Understanding how RAG provides the critical knowledge retrieval tool for intelligent agents.

Module 8: Building Your First Autonomous AI Agent

• 8.1. The Anatomy of an AI Agent: Deconstructing the core reasoning loop .
• 8.2. Giving Your Agent Superpowers: Enabling it to browse the web, run code, and use APIs as tools.
• 8.3. Creating an Agent with Memory: Implementing short-term and long-term memory.
• *8.4. Project: Build a Research Assistant Agent that can autonomously browse the web and summarize findings on any topic.

Module 9: Building Multi-Agent Systems with LangGraph

• 9.1. The Limits of Single Agents and the Need for Graphs.
• 9.2. Introduction to LangGraph: Defining states, nodes, and edges to orchestrate complex agentic workflows.
• 9.3. Designing for Control: Implementing Human-in-the-Loop interruptions for critical oversight.
• *9.4. Project: Build a Collaborative AI Team where a planner agent delegates tasks to specialized worker agents.

Module 10: Making Agents Smarter: Advanced Data Techniques

• 10.1. Graph RAG: Equipping your agents with knowledge graphs to understand complex relationships.
• 10.2. Multi-Modal Agents: Building systems that can process and integrate text, images, and other data types.

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PART III: PRODUCTION, EVALUATION, AND MASTERY

Module 11: Taking Your Agents to Production: Deployment and Scaling

• 11.1. Architecting for Scale: Designing your AI agent as a robust microservice.
• 11.2. Cloud Deployment Mastery: Hosting and scaling your agent on a cloud platform like AWS.
• **11.3.**Introducing the MCP (Model Control Plane) Server concept for efficiently deploying and managing your AI microservice.
• 11.4.The Art of Fine-Tuning: Customizing a base model to create a specialized agent.

Module 12: Evaluating and Debugging Your Agents

• 12.1. Observability for LLMs: Using LangSmith to trace, monitor, and debug your agent's reasoning process.
• 12.2. Measuring Agent Performance: Key metrics for evaluating the success and reliability of agentic systems.
• 12.3. Automated Evaluation: Implementing the powerful "LLM as a Judge" technique to score your agent's outputs.

Module 13: Capstone Project: Build a Full-Stack Autonomous AI Agent

• Build the Major project using All knowledge of GenAI