Generative AI Professional Bootcamp

Generative AI Research Bootcamp

Master Generative AI and LLMs. Work on industry-level LLM projects. Publish LLM research papers. From foundations to RAG systems and multimodal models, build production-grade AI with Python and LangChain.

30 TopicsPythonResearch Projects
Enroll Now Watch Intro

Instructors from

MIT
IIT Madras
Purdue University
85+
Students Trained
15+
Hands-on Sessions
3
MIT/Purdue PhDs
100%
Research-Grade Content
Accepted Venues

Where Our Researchers Publish & Present

Research from our cohorts has been accepted, presented, and archived across leading venues in machine learning, scientific computing, and applied AI.

NeurIPS
ICLR
AAAI
ACM
JuliaCon
IEEE eScience
EGU General Assembly
FastML
MIT URTC
arXiv
EMNLP
NeurIPS
ICLR
AAAI
ACM
JuliaCon
IEEE eScience
EGU General Assembly
FastML
MIT URTC
arXiv
EMNLP
The GenAI Boom

Generative AI Is Moving From Research to Infrastructure

Large Language Models and GenAI are shifting from academic papers to foundational infrastructure for every industry. Here is why the 2024-2026 window matters.

$280B+

Projected ML market by 2030, up from ~$55-75B in 2024 (CAGR above 30%), with GenAI emerging as a key industrial segment.

Market Research ›

ChatGPT + LangChain

OpenAI's ChatGPT and LangChain have become the standard infrastructure for building LLM-powered applications across industries.

OpenAI ›

RAG is the New Standard

Retrieval-Augmented Generation has become the default architecture for enterprise AI, combining LLMs with domain-specific knowledge bases.

RAG Architecture ›

Jobs at High-Tech Companies

Companies like Google, Meta, and Amazon have dedicated GenAI divisions, and firms across industries are actively hiring LLM and prompt engineering specialists.

LinkedIn GenAI Jobs ›
Tour the GenAI Bootcamp
Watch

Tour the GenAI Bootcamp

Applications

What You Can Build with Generative AI

From RAG-powered chatbots to multimodal vision-language systems, this bootcamp equips you to build production-grade GenAI applications across industries.

RAG-powered Chatbot System

RAG-Powered Chatbot

LLM Agent with LangChain

LLM Agents with LangChain

Generative AI Research Bootcamp Overview

GenAI Research Projects

Four Core Pillars

The Foundations of Generative AI

We teach four interconnected pillars that form the foundation of modern Generative AI. Each represents a critical capability for building, deploying, and researching LLM-powered systems.

LLM Foundations and Fine-Tuning

Understand the full LLM evolutionary tree, from BERT to GPT to Flan-T5. Learn to fine-tune models for classification, sentiment analysis, and topic modeling. Deploy models locally using Hugging Face and build practical pipelines with the ChatGPT API.

Prompt Engineering and LangChain

Master prompting fundamentals through advanced methods: in-context learning, Chain-of-Thought, and Tree-of-Thought reasoning. Build complex LLM applications using LangChain chains, memory modules, guardrails, and autonomous agents from scratch.

Semantic Search and RAG Systems

Build end-to-end Retrieval Augmented Generation pipelines. Learn dense retrieval with LLM embeddings, chunking strategies, reranking methods, and evaluation metrics (MAP/nDCG). Create robust, production-grade RAG chatbots for real-world use cases.

Multimodal Language Models

Explore Vision Transformers (ViTs), understand how they differ from CNNs, and learn vision-language models including CLIP, BLIP-2, and LLaVA. Build multimodal AI systems that bridge text, images, and structured data.

LLM Safety and Guardrails

Learn to set constraints and safety measures for LLM outputs. Implement guardrails for quality control, understand model quantization (8/4-bit, GPTQ/AWQ) for efficient deployment, and build responsible AI systems.

Research and Publication

Work on industry-level LLM research projects aimed at publication. Learn to formulate research problems, design experiments, validate hypotheses, and write scientific papers for conferences and journals.

Visual Framework

How Generative AI Works

Publication-quality diagrams illustrating the core systems you will master in this bootcamp.

The GenAI Landscape

A high-level overview of the four core pillars: LLM Foundations and Fine-Tuning, Prompt Engineering with LangChain, Semantic Search and RAG Systems, and Multimodal Language Models.

Generative AI landscape showing LLM foundations, prompt engineering, RAG, and multimodal models

RAG Pipeline Architecture

The complete Retrieval-Augmented Generation pipeline: document ingestion, chunking, embedding, vector storage, dense retrieval with cross-encoder reranking, and LLM-based answer generation.

RAG pipeline architecture from document ingestion to answer generation

LLM Agent Architecture

An LLM Agent built with LangChain showing the central reasoning engine, tool calling, short-term and long-term memory, chain orchestration, and the Observe-Think-Act reasoning loop.

LLM Agent architecture with tools, memory, and reasoning loop
Who Is This For

Designed for Researchers, Engineers, and Professionals

Whether you come from computer science, engineering, data science, or any other field, this bootcamp teaches you to build and research with the latest Generative AI tools and techniques.

Graduate Researchers

PhD students and postdocs who want to apply LLMs to their research domain, build RAG systems for literature analysis, or publish papers on Generative AI topics.

PhD StudentsPostdocsResearch Scholars

Software Engineers

Developers looking to integrate LLMs, RAG pipelines, and AI agents into production applications using LangChain, Hugging Face, and modern LLM APIs.

Backend EngineersML EngineersFull-Stack Developers

Data Scientists

ML practitioners who want to move beyond traditional models and build LLM-powered systems for text clustering, topic modeling, semantic search, and content generation.

ML EngineersNLP SpecialistsApplied Scientists

Industry Professionals

Business leaders, product managers, and consultants who want to understand and leverage Generative AI for strategic decision-making and product development.

Product ManagersConsultantsTechnical Leaders
Curriculum

A Guided Journey from Foundations to Research

30 topics spanning LLM foundations, prompt engineering, RAG systems, LangChain agents, and multimodal models.

LLM Foundations & Hands-on ProjectsTopics 1-9
Session 1

Hands-on LLMs: Series Intro

Led by

  • Overview of LLMs, applications, course structure
EXERCISE:
Session 2

The LLM Evolutionary Tree

Led by

  • History, milestones, architecture evolution
EXERCISE:
Session 3

Running Microsoft Phi-3 using Hugging Face

Led by

  • Practical deployment of Microsoft Phi-3 model locally using Hugging Face APIs
EXERCISE:
Session 4

Fine-tune BERT (Sentiment)

Led by

  • Hands-on session on fine-tuning BERT to perform sentiment analysis
EXERCISE:
Session 5

Flan-T5 for Classification

Led by

  • Prompt formatting, generative vs discriminative
EXERCISE:
Session 6

Using ChatGPT API for Movie Review Classification

Led by

  • Utilize OpenAI's ChatGPT API to build a practical sentiment classification pipeline
EXERCISE:
Session 7

Text Clustering using Sentence-Transformers

Led by

  • Implementing text clustering techniques on ArXiv research papers
EXERCISE:
Session 8

Topic Modeling with BERTopic

Led by

  • Hands-on project applying BERTopic to identify themes from ArXiv research papers
EXERCISE:
Session 9

LLMs for Text Clustering and Topic Modeling

Led by

  • Identification on textual datasets, LLM-assisted clustering
EXERCISE:
Prompt Engineering & LangChainTopics 10-17
Session 10

Intro to Prompt Engineering

Led by

  • Fundamentals of crafting effective prompts for maximizing LLM outputs
EXERCISE:
Session 11

Advanced Prompt Engineering

Led by

  • Deep dive into in-context learning, Chain-of-Thought, and Tree-of-Thought methods
EXERCISE:
Session 12

LLM Guardrails

Led by

  • Techniques to set constraints, safety measures
EXERCISE:
Session 13

LangChain & Agents (Intro)

Led by

  • Intro to building complex LLM applications and Agents using LangChain
EXERCISE:
Session 14

LLM Quantization

Led by

  • Understanding model quantization methods for efficient deployments of LLMs
EXERCISE:
Session 15

Coding Chains (LangChain)

Led by

  • Hands on demonstration of creating coding chains using LangChain
EXERCISE:
Session 16

How to give Memory to LLMs

Led by

  • Techniques for implementing short-term and long-term memory in LLM applications
EXERCISE:
Session 17

Code your First LLM Agent using LangChain

Led by

  • Step-by-step project to build a functioning LLM-powered agent
EXERCISE:
Semantic Search & RAGLectures 18-25
Session 18

Semantic Search & RAG

Led by

  • Basics of semantic search and RAG concepts
EXERCISE:
Session 19

Coding an LLM Dense Retrieval System

Led by

  • Build a practical dense retrieval system using LLM embeddings
EXERCISE:
Session 20

Chunking Strategies for LLM

Led by

  • Effective strategies for breaking down text into meaningful chunks for retrieval and processing
EXERCISE:
Session 21

Reranking for Semantic Search

Led by

  • Understand and implement reranking methods
EXERCISE:
Session 22

Evaluating Retrieval Systems

Led by

  • Measure retrieval effectiveness using MAP and nDCG
EXERCISE:
Session 23

RAG: Intro & Coding

Led by

  • Hands-on implementation of basic RAG systems
EXERCISE:
Session 24

Advanced RAG

Led by

  • Explore and apply advanced methodologies in RAG for improving accuracy and context management
EXERCISE:
Session 25

Evaluating RAG Systems

Led by

  • Best practices and metrics for assessing the performance of RAGs
EXERCISE:
Multimodal Language ModelsLectures 26-30
Session 26

Vision Transformers: How and Why They Work?

Led by

  • Understand ViTs, how they differ from CNNs
EXERCISE:
Session 27

Intro to CLIP

Led by

  • Explore how CLIP bridges vision and language
EXERCISE:
Session 28

Intro to BLIP

Led by

  • Learn how BLIP enhances text generation
EXERCISE:
Session 29

Multimodal LLMs: 30-Minute Summary

Led by

  • A concise overview of Multimodal LLMs
EXERCISE:
Session 30

Series Summary

Led by

  • Reviewing the concepts covered, key takeaways
EXERCISE:
What You Get

Research-Grade Deliverables

Everything you need to go from GenAI beginner to building production-grade LLM systems and publishing research.

Complete Python Codebase

Production-ready Python code for every session, including LLM fine-tuning, RAG pipelines, LangChain agents, and multimodal applications.

  • All lecture code files and Jupyter notebooks
  • Homework assignments with solutions
  • Research project starter templates
  • Fully documented LLM pipelines

Lecture Notes and Videos

Lifetime access to all session recordings and comprehensive lecture notes covering every Generative AI concept.

  • HD video recordings of all sessions
  • Detailed lecture notes in PDF format
  • Annotated code walkthroughs
  • Reference material and reading lists

Research Project Portfolio

Industry-level GenAI projects including RAG systems, LLM agents, and multimodal applications ready for your portfolio or publication.

  • End-to-end RAG chatbot system
  • LLM agent with tool calling
  • Multimodal AI application
  • Publication-ready research results

Community and Mentorship

Join the Vizuara GenAI community on Discord for ongoing collaboration, doubt clearance, and research partnerships.

  • Discord community access
  • Student collaboration opportunities
  • Assignment checking and doubt clearance
  • Free access to all ML webinars
Your Instructors

Learn from MIT and Purdue AI PhDs

Our instructors are co-founders of Vizuara AI Labs and published researchers in AI and Machine Learning, with expertise spanning LLMs, scientific computing, and applied Generative AI.

Dr. Raj Dandekar
MIT PhD
LLM Foundations, RAG Systems, LangChain Agents, and Advanced Prompt Engineering

Dr. Raj Dandekar

Co-founder, Vizuara AI Labs

PhD from MIT, B.Tech from IIT Madras. Dr. Raj specializes in building LLMs from scratch, including DeepSeek-style architectures. His expertise spans AI agents, scientific machine learning, and end-to-end model development.

MIT
IIT Madras
LinkedIn Scholar
Dr. Rajat Dandekar
Purdue PhD
Fine-Tuning, LLM Quantization, Vision Transformers, and Multimodal Models

Dr. Rajat Dandekar

Co-founder, Vizuara AI Labs

PhD from Purdue University, B.Tech and M.Tech from IIT Madras. Dr. Rajat brings deep expertise in reinforcement learning and reasoning models, focusing on advanced AI techniques for real-world applications.

Purdue University
IIT Madras
LinkedIn Scholar
Dr. Sreedath Panat
MIT PhD
SLM Deployment, Semantic Search, Coding Chains, and CLIP

Dr. Sreedath Panat

Co-founder, Vizuara AI Labs

PhD from MIT, B.Tech from IIT Madras. 10+ years of research experience. Dr. Panat brings deep technical expertise from both academia and industry to make complex AI concepts accessible and practical.

MIT
IIT Madras
LinkedIn Scholar
Build a DeepSeek Model (From Scratch)

Manning #1 Best-Seller

Build a DeepSeek Model (From Scratch)

By Dr. Raj Dandekar, Dr. Rajat Dandekar, Dr. Sreedath Panat & Naman Dwivedi

Credentials

Learn from MIT PhD Researchers

Our lead instructor Dr. Raj Dandekar holds a PhD from MIT, where he conducted research at the Julia Lab under Prof. Alan Edelman and Chris Rackauckas. Our team brings deep expertise in LLMs, scientific computing, and applied AI research.

MIT Certificate of Dr. Raj Dandekar
Our Research

Recent Papers From Our Research

A few recent GenAI and LLM papers from our research over the past years. Students in the Industry Professional plan work on similar projects aimed at publication.

Beyond Passive Viewing: A Hybrid Learning Platform Augmenting Video Lectures with Conversational AI

Beyond Passive Viewing: A Hybrid Learning Platform Augmenting Video Lectures with Conversational AI

Mohammed Abraar, Raj Dandekar, Rajat Dandekar, Sreedath Panat

AI EducationNLPConversational AI
Accepted atTeachNLP Workshop · 2026
Multilingual Evaluation of Human vs. AI Text Classification with Zero-Shot Analysis of Contemporary LLM Architectures

Multilingual Evaluation of Human vs. AI Text Classification with Zero-Shot Analysis of Contemporary LLM Architectures

Pranamya Nilesh Deshpande, Raj Dandekar, Rajat Dandekar, Sreedath Panat

NLPMultilingualAI Detection
Accepted atLM4UC Workshop · 2026
Regional-TinyStories: A Small Language Model Framework for Evaluating Language Learning, Tokenizers, and Datasets

Regional-TinyStories: A Small Language Model Framework for Evaluating Language Learning, Tokenizers, and Datasets

Nirvan Patil, Malhar Abhay Inamdar, Agnivo Gosai, Guruprasad Pathak, Anish Joshi, Anish Joshirao, Raj Dandekar, Rajat Dandekar, Sreedath Panat

SLMsMultilingualLow-Resource NLP
Accepted atAACL-IJCNLP · 2025
Unifying Mixture of Experts and Multi-Head Latent Attention for Efficient Language Models

Unifying Mixture of Experts and Multi-Head Latent Attention for Efficient Language Models

Sushant Mehta, Raj Dandekar, Rajat Dandekar, Sreedath Panat

LLMsMoEEfficient LLMs
Accepted atBabyLM Workshop · 2025
Read More Papers
From the Community

Stories from our researchers

Milestones, acceptances, and moments shared by Vizuara students and alumni on LinkedIn.

Pricing

Enroll in the Bootcamp

Choose the plan that matches your goals, from self-paced learning to intensive research mentorship with MIT PhDs.

Student Plan

Rs 30,000

Save 25%. Originally Rs 40,000.

Enroll Now
  • Lifetime access to all videos, code files, and homework assignments
Most Popular

Industry Professional

Rs 1,25,000

Save 17%. Originally Rs 1,50,000. MIT and Purdue PhDs as your research mentors.

Enroll Now
  • Lifetime access to all videos, code files, and homework assignments
  • Access to bootcamp community on Discord
  • Assignment checking and doubt clearance
  • Free access to all ML webinars throughout the year
  • Access to open list of research problems in GenAI
  • 4-month personalized guidance in doing research
  • Publishing the research in conferences/journals
  • How GenAI and LLMs can be integrated in industries
FAQ

Frequently Asked Questions

Everything you need to know about the Generative AI Research Bootcamp.

Ready to Master Generative AI ?

Join hundreds of researchers and engineers who have built production-grade LLM systems and published AI research. Start building with the latest Generative AI tools and techniques.

Enroll Now Watch Video

Have more queries?

Reach out to our team on email for any questions about the bootcamp, curriculum, or application process.

research@vizuara.com
Prathamesh Joshi

If the email discussion goes well and we find the candidate genuinely interested in research, we also provide a 1-on-1 15-minute talk with our Lead AI Scientist, Prathamesh Joshi.

Prathamesh Joshi

Lead AI Scientist, Vizuara AI Labs

Prathamesh Joshi is a Lead AI Scientist at Vizuara AI Labs, with prior experience at the Max Planck Institute, Germany. His expertise spans Generative AI and Scientific Machine Learning, with a strong publication record across ICLR Workshops, IEEE conferences, and other top venues. He has also mentored students through intensive bootcamps, guiding them toward publications at NeurIPS Workshops, ICLR, JuliaCon, and AAAI Workshops.