Generative AI

This course covers how to use Generative AI with prompt engineering, augment Large Language Models (LLMs) using Retrieval Augmented Generation (RAG) and fine-tuning, and use an AI pair programmer (like GitHub Copilot).

At the end of this course, you will understand how to use Foundation Models like GPT, Gemini, DeepSeek etc. effectively and make Code Models write code in Python for you.

Course Details

Prerequisite	Basic knowledge of Python
Course Fee	Rs. 6000/- (Includes digital course material)
Digital or Physical Certificate Fee	Rs. 300/-
Source code of examples	All examples demonstrated in the classroom are made available through github repository

Detailed Syllabus

Introduction to AI

What is AI - History and Current Trends
Machine Learning, Deep Learning and Reinforcement Learning
Implementation of Machine Learning
Introduction to Generative AI, Foundation Models and Large Language Models
LLMs, size and different options
Image Models and Code Models
Limitations of LLMs

Prompt Engineering

What is Prompt and Prompt Engineering
Different ways to give prompts
Different use cases of LLMs - summarization, analysis, translation etc.
Prompting Patterns - Audience Prompt, Root Prompt, Few-Shot Prompt, Role-based prompt etc.

Retrieval Augmented Generation (RAG)

What is Retrieval Augmented Generation (RAG)
What is HuggingFace?
What is LangChain?
How to use Python to access LLMs deployed in HuggingFace
Different concepts related to RAG - Vectorization, Embeddings, Similarity Search etc.
Implementing RAG using HuggingFace Foundation Models and LangChain
Introduction to Fine-Tuning
How to implement Fine-Tuning

AI Pair Programming

What is AI Pair Programming
How to use Github Copilot or Gemini Code Assist with Python and other languages
How to integrate AI Pair Programmer with IDEs
How to use Chat feature to generate code
How to enhance code generated by AI Pair Programmer
How to use participants and slash commands
How to generate documentation using Copilot
How to generate test cases