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Dur: 3 months
Course fee : 20000 /-

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Advanced Generative & Agentic AI

Course Overview

The Advanced Generative AI and Agentic AI course offers an in-depth, industry-aligned journey into the next frontier of artificial intelligence autonomous agents and advanced generative systems. This program is designed to transform learners into AI professionals capable of building self-operating AI agents, multi-model reasoning systems, and end-to-end AI solutions using cutting-edge frameworks like LangChain, OpenAI, Hugging Face, CrewAI, and AutoGen.

Through hands-on projects and guided mentorship, students will master Large Language Models (LLMs), multi-agent collaboration, autonomous workflow design, retrieval-augmented generation (RAG), and AI tool orchestration for real-world applications. The curriculum bridges theory, implementation, and deploymentmaking learners ready for AI-powered product development, research, and enterprise innovation.

Description

This advanced-level program builds upon foundational AI knowledge and expands into autonomous and generative intelligence. Learners will explore the mechanics behind AI agents that plan, reason, and act, and understand how to integrate these agents with APIs, databases, and external tools.

By the end of this course, participants will have developed deployable AI systems that can think, decide, and execute enabling next-generation applications like AI copilots, autonomous research bots, intelligent chat systems, and agentic workflow automations.

Key Highlights

Generative Foundations: Reinforce concepts in NLP, LLMs, and transformer-based architectures.
Agentic AI Systems: Learn how autonomous AI agents plan, reason, and collaborate to achieve tasks.
LangChain & CrewAI Mastery: Build multi-agent pipelines for reasoning, execution, and task delegation.
Advanced RAG Systems: Combine LLMs with real-time data retrieval and vector databases.
Tool & API Integration: Equip agents with browsing, code-execution, and data-analysis abilities.
Multi-Agent Collaboration: Implement agent hierarchies Manager, Researcher, Executor for complex tasks.
Production AI Deployment: Learn containerized deployment with Docker, Flask, and AWS.
Industry Projects: Develop 10+ real-world agentic AI applications across domains.

Course Objectives

Technical Skills

Build and deploy autonomous AI agents using LangChain, CrewAI, and OpenAI tools.
Master prompt chaining, memory architectures, and tool invocation in agent design.
Create multi-agent workflows for data research, content generation, and customer automation.
Implement Retrieval-Augmented Generation (RAG) pipelines with FAISS or Pinecone.
Integrate vector databases, embeddings, and LLM fine-tuning for domain-specific AI.
Develop AI APIs and dashboards for live, interactive agent-based applications.
Apply monitoring, logging, and evaluation frameworks for deployed AI systems.

Practical Applications

Build intelligent copilots for research, coding, and business analysis.
Develop autonomous AI assistants that execute multi-step workflows.
Design domain-specific chatbots with persistent memory and reasoning.
Implement document understanding and context-driven Q&A systems.
Create decision-making AI models for finance, healthcare, or customer support.
Deploy production-grade agentic AI systems with real-time monitoring.

Strategic Understanding

Understand the evolution from LLMs → RAG → Agentic AI.
Explore human-AI collaboration and autonomous reasoning in enterprise workflows.
Analyze ethical implications, system safety, and performance optimization.
Design scalable architectures for AI-driven automation pipelines.

Prerequisites

- Familiarity with Python programming and machine learning basics.

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Course Curriculum

Introduction-What We will Learn In This Course
What is Generative AI?
What is Agentic AI?
Generative AI vs Agentic AI

Basics of Generative AI bloack chapter
What are LLMs?
Key Concepts

Tokenization
Embeddings
Vocabulary
Attention

Transformer Architecture

Self-Attention Mechanism
Multi-head attention
Cross Attention
Encoder – Decoder

Open Source Vs Closed Source LLMs
HuggingFace Ecosystem

Model Loading
Model Parameters
Model Weight Formats (.pth, safetensors, onnx)
Model Size calculation and Licence

Multi-modal LLMs: Text, Audio (ASR, TTS), Image, Video

Prompts and Context

Max Sequence Length Vs Max Output Tokens
Task-specific Prompts

Sampling Parameters (Temperature, Top-k, Top-p, etc.)
Prompting Techniques

Zero-Shot Learning
Chain of Thought (CoT)
ReAct

Guardrails
Using Chat Completion APIs

OpenAI (ChatGPT)
Google Gemini
Anthropic Claude

What is RAG?
LLM Hallucination: Causes and Mitigation
When to Use RAG
Components of RAG:

Embeddings
Vector Databases: Chroma, Pinecone, FAISS, Milvus
Chunking Strategies
Conversational RAG
Embedding Spaces: Semantic Similarity & Cosine Distance
Answer Grading / Response Evaluation (BLEU, ROUGE, GPT-based)

Corrective RAG (CRAG)
Self-RAG with Reflection
Graph RAG
Hybrid RAG(Semantic + Keyword)

Graph Fundamentals – Nodes, Edges
Ontology Design
GraphDBs:

Advantages of GraphDB
Neo4j: Community vs Enterprise vs Cloud

Introduction to LangChain
Chains, Prompts, and Templates
Memory Systems and Conversation Flow

Memory Types: Short-term, Long-term, Episodic o Persistence Strategies
Persistence Strategies

Basic Document Loaders and Text Splitters
Tool Calling and Structured Output
Output Parsers
Building RAG Pipelines with LangChain

What is Agentic AI?
AI Agents vs Agentic AI
Agentic AI Frameworks Overview

No-code vs Code-first
N8n vs LangGraph vs Airflow

Design Principles:

Goal, Planner, Orchestrator
Copilot vs Autopilot
Human in loop

Agentic AI Frameworks

CrewAI, LangGraph, AutoGen

Agentic AI using LangGraph
LangChain vs LangGraph
LangGraph Components
Workflow Types:

Sequential
Parallel
Iterative
Conditional

Memory & State Management

Persistence Strategies
Time Travel in LangGraph

Observability with LangSmith

What is Observability?

MCP Fundamentals and Architecture
MCP Server and Client Implementation
Tool Integration through MCP
MCP vs Traditional Integration Patterns

A2A Communication Fundamentals
Orchestration Patterns:

Manager-Worker
Peer-to-Peer

When to Use Fine-tuning vs Prompt Engineering
Parameter-Efficient Fine-Tuning (PEFT)

LoRA
QLoRA

Quantization Techniques

Intro to Quantization
Asymmetric vs Symmetric
Post-training Quantization vs Quantization-Aware

Model Serving Frameworks:

vLLM
TensorRT-LLM

Domain-Aware LLM Chatbot Using Open-Source and API-Based Models
Customer Support Assistant Powered by Retrieval-Augmented Generation (RAG)
Agentic AI Advisor for Healthcare (or Domain-Specific) Guidance and Decision Support
Stock Market Insight Generation Using Agentic AI and Web Search Tools

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Who can learn this course

For Beginners

Learners who understand Python and want to advance into AI automation.
Students aspiring to work in AI agent development and applied research.

For Professionals

Software engineers upgrading from standard AI to Agentic Intelligence.
Data scientists and ML engineers expanding into autonomous AI workflows.
AI developers aiming to master LangChain, CrewAI, and OpenAI toolkits.

For Business & Domain Experts

Product managers designing AI-first platforms.
Entrepreneurs building AI-powered startups or intelligent digital assistants.
Analysts and consultants seeking AI automation and decision support skills.