Generative AI Mastery

Comprehensive Curriculum

Module 1: Foundations

Weeks 1-3 • Build your AI knowledge base

Week 1

Foundations of Generative AI

1.1 Course overview, Intro to Generative AI, AI vs ML vs DL

1.2 Key terminologies: LLM, Embedding, Prompt Engineering

→ Core concepts with OpenAI SDK, Hugging Face Transformers & Ollama

Week 2

AI Solution Patterns & Prompting

2.1 AI Solution Design Patterns

2.2 Prompt Engineering - Detailed

→ LangChain, LlamaIndex RAG workflows, zero-shot & few-shot prompting

Week 3

Hands-on Prompting & Recap

3.1 Hands-on: Writing Prompts

3.2 Module-1 Recap

→ Business prompts (email, summarization), embeddings with Sentence-Transformers

Module 2: RAG & Vector Search

Weeks 4-6 • Build intelligent knowledge systems

Week 4

Advanced Prompting & Embeddings

4.1 Advanced Prompt Techniques

4.2 Embedding - Detailed

→ LangChain + PydanticAI, Langfuse evaluation, Sentence-Transformers

Week 5

Vector Databases & RAG

5.1 Vector Databases

5.2 RAG Workflow

→ Qdrant integration, LlamaIndex knowledge indexing, LangChain pipelines

Week 6

QnA Bot & Extensions

6.1 Hands-on: QnA Bot

6.2 Module 2 Recap & Hands-on

→ Multi-document retrieval, Langfuse evaluation, LangChain + HF + LlamaIndex

Module 3: Fine-Tuning & Customization

Weeks 7-9 • Tailor AI to your specific needs

Week 7

Fine-Tuning Foundations

7.1 Fine-Tuning - Detailed

7.2 Data Preparation

→ PEFT (LoRA), BitsAndBytes quantization, Hugging Face Datasets

Week 8

Synthetic Data & Customization

8.1 Synthetic Data Creation

8.2 Customizing Models

→ LangChain + CrewAI data generation, Ollama-hosted LLMs adaptation

Week 9

Applied Fine-Tuning

9.1 Module 3 Recap & Hands-on

9.2 Project Extension

→ Hugging Face Trainer & PEFT, fine-tuned QnA bot enhancement

Module 4: Agents & Deployment

Weeks 10-12 • Build autonomous AI systems

Week 10

AI Agent Architecture

10.1 AI Agent Architecture

10.2 Agent with RAG

→ CrewAI + LangChain Agents, OpenAI Agent SDK integration

Week 11

Tools & Deployment

11.1 Tool Integration

11.2 Deployment Techniques

→ Agno, LangChain tools, CrewAI toolchains, FastAPI + Docker + Ollama

Week 12

Capstone Build & Showcase

12.1 Capstone Build

12.2 Final Recap, Testing & Hands-on

→ Full AI Agent: LangChain + CrewAI + LlamaIndex + Qdrant + Langfuse

Frequently Asked Questions

Not strictly, but familiarity with Python and basic ML concepts will help. We'll cover Generative AI foundations before diving into advanced topics.

The course is entirely in Python (3.10+), with tools like LangChain, CrewAI, LlamaIndex, Hugging Face Transformers, Qdrant, Ollama, OpenAI Agent SDK, Langfuse, and deployment with FastAPI + Docker.

A GPU is recommended for fine-tuning and large model experiments. However, most exercises can be run on CPU or free GPU platforms like Google Colab. For production, you'll learn to integrate APIs (OpenAI, Hugging Face, Ollama).

A prompting library for business use cases
A QnA bot using RAG (LangChain + Qdrant + Hugging Face)
Fine-tuned models for domain tasks
A multi-agent AI system with memory, RAG, and tool integration

Focused on hands-on implementation (not just theory)
Covers modern AI agent frameworks (CrewAI, OpenAI Agent SDK, Langfuse monitoring)
Step-by-step capstone project that integrates multiple tools

We provide Docker setups and cloud-friendly alternatives (Colab, VS Code remote) so you can run all projects regardless of local environment.

Yes. You'll learn to use OpenAI APIs and Ollama for running models locally (e.g., Llama-3). This ensures you can build both cloud-native and self-hosted AI systems.

On average, 6–8 hours/week split between lectures, reading, coding exercises, and project work.

Yes. On completing the capstone project showcase, you'll receive a Certificate of Completion recognizing your Generative AI mastery.

By Week 12, you'll have built a production-ready AI Agent system with:

Memory
RAG (knowledge-grounded answers)
Multi-agent orchestration
Tool integration (APIs, external systems)
Monitoring with Langfuse
All deployed in Docker/FastAPI for real-world usage

Expert Instruction

SRI SANTH M

AI Technology Architect & Educator with a passion for democratizing artificial intelligence. As the founder of innovative AI solutions including Hipocap (workflow automation platform) and GlideIN (LinkedIn automation suite), I bring real-world expertise to the classroom.

With over 300 students successfully trained in AI and technology, I specialize in making complex AI concepts accessible and immediately applicable to real business challenges. My teaching approach combines theoretical foundations with hands-on project experience.

AI Architecture 300+ Students Trained Product Development

AI-Powered Applications Built by Instructor

Hipocap

AI Workflow Automation Platform

Intelligent automation platform that connects different applications and streamlines complex workflows using AI-driven decision making.

Visit Platform

GlideIN

LinkedIn Automation Suite

Advanced LinkedIn automation tool powered by AI for intelligent networking, lead generation, and professional relationship management.

Visit Platform

Tools & Prequisites needed for the course

API Keys & Tokens

OpenRouter / OpenAI

Hugging Face Token

Langfuse

Frameworks & SDKs

LangChain

CrewAI

LlamaIndex

OpenAI Agent SDK

PydanticAI

Models & Libraries

HF Transformers

Sentence-Transformers

Ollama (Local LLMs)

Qdrant Vector DB

Development Tools

VS Code / Cursor

Docker Desktop

Git & GitHub

Python 3.10+