Overview
The Generative AI Lab: Create Smarter Systems and Smarter Ideas is designed to provide hands-on experience in building intelligent systems capable of generating new content such as text, images, and code. This lab focuses on the practical implementation of modern generative models including Large Language Models (LLMs), diffusion models, and GANs.
Participants will explore how generative AI is transforming industries by enabling creativity, automation, and innovation. The course emphasizes real-world applications such as chatbots, image generation, content creation, and AI-powered assistants. Through guided experiments and projects, learners will develop a strong foundation in designing, training, and deploying generative AI systems.
By the end of the lab, students will be able to create intelligent applications, understand ethical considerations, and leverage AI tools to solve complex problems creatively and efficiently.
Our Trainers
Mr. Vikas Sarkar
Experience:21 years
Qualfication: Mtech(CSE)
Technology: C,C++,Data Structure,Dotnet,SQL,Advance PythonAI/ML Gen AI,Agentic AI and CCNA certified
Modules
Module 1 :Introduction of Python
Roadmap of GenAI
What is Core Python
Lab:Variable,Datatypes & Operators
Lab:Condition Statement
Lab:Control Flow Statement
Module 2 :Introduction of ML & DL
Supervise Learning
Unsupervise Learning
Neural Network
AI Tools
Module 3 :GenAI MODEL
Introduction of GenAI
Foundation Model of GenAI
Builder Perspective
User Perspective
Introduction of LLM
Contents of LLM
Module 4: Prompt Engineering
Discriminative vs. Generative AI
AI Evolution: RNN, LSTM & Transformers
Attention Mechanism Explained
Tokens & Cost Analysis
Context Windows & Memory Limits
Module 5: Structured Prompting (RCTNO)
Lab: Marketing Content (ChatGPT)
Lab: Cold Emails for Internships
Lab: Big Data Analysis (Gemini)
Zero-Shot & Few-Shot Techniques
Chain of Thought (CoT) & Hallucinations
Logic Problem Lab (Math & Reasoning)
Module 6: Advanced Frameworks
ReAct (Reason + Act) Framework
Tree of Thoughts (ToT) Strategy
Directional Stimulus (Hinting)
Iterative Development (Image Gen Lab)
Module 7: Professional Automation
Data Extraction & Code Refactoring
RAG (Retrieval-Augmented Generation)
Lab: PDF Analysis
ATS Resume Building
Lab: AI Slide Generation (NotebookLM)
Module 8: Installation of Anaconda & Langchain Model
Opensource Model
Closed Source Model
Module 9: Chatboat Implementation using different Models
Lab:OpenAI
Lab:Anthropic
Lab:Gemini
Lab:Hugging face
Module 10: Gen AI Vs AI Agent VS Agentic AI
Outcomes
CO1: Understand key concepts of generative AI (LLMs, GANs, diffusion models).
CO2: Build models to generate text, images, and code.
CO3: Develop applications like chatbots and AI assistants.
CO4: Evaluate and deploy generative AI systems.
CO5: Apply ethical and responsible AI practices.
FAQs
Q1: What is Generative AI?
Generative AI refers to models that can create new content such as text, images, code, and audio.
Q2: What will I learn in this lab?
You will learn to build and use models like LLMs, GANs, and diffusion models for real-world applications.
Q3: Do I need prior knowledge of AI?
Basic knowledge of Python and machine learning is helpful but not mandatory.
Q4: What tools or technologies will be used?
Tools like Python, TensorFlow/PyTorch, and APIs for generative AI models will be used.
Q5: What kind of projects will be done?
Projects include chatbots, image generators, content creation tools, and AI assistants.
Q6: How is this course useful?
It helps in building skills for careers in AI, data science, and software development.
Q7: Are there any ethical considerations?
Yes, the course covers responsible AI use, bias, and data privacy.
