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Mastering Generative AI & Agentic AI: From LLMs to Multi-Agent Systems

By the end of this course, you will have both theoretical understanding and practical skillsets to build production-grade Gen AI & Agentic AI solutions for real-world business problems.
  • Training Type
    Live Training
  • Category
    Artificial Intelligence
  • Duration
    8 Hours
  • Rating
    4.8/5
Mastering Generative AI & Agentic AI: From LLMs to Multi-Agent Systems

Course Introduction

About the Course

This comprehensive hands-on course takes you from the fundamentals of AI, ML, and Deep Learning to advanced real-world applications of Generative AI (Gen AI) and Agentic AI. You will learn how to build and deploy Gen AI applications using LangChain, Vector Databases, RAG architectures, AI Agents, and Multi-Agent Systems with live projects. The course also covers emerging protocols like MCP, A2A, reasoning models, safety evaluation, fine-tuning with LoRA & QLoRA, and addresses ethical challenges in Generative AI.

Course Objective

AI Family Tree: ML, DL, Gen AI, Agentic AI - clear conceptual foundations

Understand LLMs, how they work, context window, sampling methods

Build Gen AI applications using LangChain, Prompt Engineering, Groq, Ollama

Work with Vector Databases: ChromaDB - storing, querying, filtering embeddings

Build full RAG (Retrieval Augmented Generation) pipelines

Create real-world business projects like:

Real Estate Assistant (RAG)

E-commerce chatbot with routing, database, and web scraping

Build Agentic AI applications using:

Llama + Agno framework

Reasoning Agents

Multi-Agent Systems

Model Context Protocol (MCP)

Agent-to-Agent Protocol (A2A)

Agentic AI evaluation frameworks - functional, safety, and operational metrics

Fine-tuning LLMs using LoRA, QLoRA, and Unsloth for custom domain applications

Ethics, legal, privacy, hallucination, bias, and environmental concerns in Gen AI

Who is the Target Audience?

Data Scientists and ML Engineers who want to upgrade their skills in Generative AI and Agentic AI.

Software Developers and Python Programmers who want to build real-world Gen AI applications using LangChain, Agno, MCP, and other modern frameworks.

AI Enthusiasts who want a complete practical roadmap from fundamentals to advanced Gen AI implementations.

Tech Founders, CTOs, and Product Managers seeking to understand Gen AI capabilities, architecture, and real-world use cases to build AI-powered products.

AI Researchers and Students who want hands-on exposure to cutting-edge AI agent frameworks, multi-agent systems, and evaluation protocols.

BPM, BPO, and Enterprise Professionals who want to automate processes and build business-focused AI automation using Agentic AI frameworks.

Career switchers and freshers looking for highly industry-relevant, job-oriented AI skills with real-world projects.

Freelancers and Consultants who want to build custom Gen AI solutions for clients across domains like Real Estate, E-Commerce, Finance, and more.

Academicians and Trainers who want to stay ahead by mastering the latest Generative AI and Agentic AI technologies for teaching or content development.

Basic Knowledge

Basic understanding of Python programming

Familiarity with machine learning (supervised & unsupervised ML basics)

Some exposure to NLP will be helpful (not mandatory)

Familiarity with APIs, REST, JSON and will be an added advantage

No prior knowledge of Gen AI or LangChain is required - we will start from scratch

Related Virtual Classroom
Have Any Queries
Call Us : +510-849-6155
Email : support@simplivlearning.com

Available Batches

17 Jul 2025 Thu - Fri ( 2 Days ) 12:00 PM - 04:00 PM (Eastern Time)
21 Aug 2025 Thu - Fri ( 2 Days ) 12:00 PM - 04:00 PM (Eastern Time)
Limited period offer: 50 % additional discount on all courses, auto applied at checkout.

Pricing

Live
Single Attendee
249 416
Live Session
Recorded
Single Attendee
299 499
6 month Access for Rec
Live+Recorded
Single Attendee
349 583
6 month Access for Rec
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Curriculum
Total Duration : 8 Hours
Theoretical Fundamentals
  • AI Family Tree  
  • Essential Concepts: Machine Learning  
  • Essential Concepts: Deep Learning  
Introduction to Generative AI and Agentic AI
  • What is Generative AI?  
  • Traditional AI vs Gen AI  
  • What are AI Agents and Agentic AI?  
  • Gen AI vs AI Agents vs Agentic AI  
  • Real-world Applications for Gen AI & Agentic AI  
  • Steps to Build Gen AI and Agentic Applications  
Gen AI: Key Concepts and Foundations
  • What are Large Language Models?  
  • How LLMs Work?  
  • Context Window, Temperature, Top-p and Top-k  
  • Challenges: Hallucinations, Security and Cost  
  • What is a Vector Database?  
  • What is RAG (Retrieval Augmented Generation)?  
Gen AI: Langchain and Prompting Essentials
  • Elements of a Good Prompt  
  • Zero-Shot, One-Shot, and Few-Shot Prompting  
  • LangChain Installation  
  • Groq and Ollama Setup  
  • Calling LLM from Langchain  
  • Prompt Templates & Chains  
  • Output Parser  
  • Build Financial Data Extraction App  
Gen AI: Vector Database
  • Chromadb: Introduction and Installation  
  • Basic Operations in Chromadb  
  • Add, Update, Delete, Query  
  • Metadata Filtering  
  • Euclidean and Cosine Distance  
Gen AI: Business Project 1- Real Estate Assistant Using RAG
  • Problem Statement  
  • RAG Based Technical Architecture  
  • Document Loaders  
  • Text Splitters  
  • Store Data in Vector Store (Chroma DB)  
  • Retrieval and Answer Generation  
  • Streamlit UI  
Gen AI: Business Project 2 - E-Commerce Chatbot
  • Problem Statement  
  • SOW & Technical Architecture  
  • Implement FAQ Handling  
  • Routing using semantic-router  
  • Streamlit UI: FAQ Handling  
  • SQLite Database Setup  
  • Implement Product Handling: SQL Query Generation  
  • Implement Product Handling: Data Comprehension  
  • Streamlit UI: Product Questions Handling  
  • Web Scraping  
Agentic AI: A Hands-on Approach
  • Agency in AI  
  • Build Your First Agent using Llama and Agno  
  • Agent with Custom Tool  
  • What are Reasoning Models?  
  • Building a Reasoning Agent with Agno  
  • Multimodal Agent  
  • Other Frameworks: Smolagents, Google ADK  
Agentic AI: Architecture and Protocols
  • What is Model Context Protocol (MCP)?  
  • Build Your First MCP Server: Leave Management  
  • Prebuilt MCP Servers  
  • What is A2A Protocol?  
Agentic AI: Building Multi-Agent Systems
  • Build Your First Multi Agent Program  
  • When to consider Multi-Agent Systems?  
  • Design Patterns for Multi-Agent Systems  
  • Route Agent  
Agentic AI: Evaluation for Safety and Task Success
  • Introduction to Agentic AI Evaluation  
  • Functional Evaluation  
  • Hands on Functional Eval in Agno  
  • Safety and Guardrails  
  • Operational Metrics  
  • Hands on Perf Eval in Agno  
Fine-Tuning
  • Introduction to Fine-Tuning  
  • Low-Rank Adaptation (LoRA)  
  • Quantization Basics  
  • QLoRA  
  • Fine-Tuning Llama with Unsloth  
Ethics in Gen AI
  • Ethical Challenges in Gen AI  
  • Hallucination and Misinformation  
  • Bias  
  • PII and Privacy Laws  
  • Environmental Impact  
  • Copyrights and Intellectual Property