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.
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Tour the GenAI Bootcamp
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
LLM Agents with LangChain
GenAI Research Projects
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.
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.
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.
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.
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.
Software Engineers
Developers looking to integrate LLMs, RAG pipelines, and AI agents into production applications using LangChain, Hugging Face, and modern LLM APIs.
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.
Industry Professionals
Business leaders, product managers, and consultants who want to understand and leverage Generative AI for strategic decision-making and product development.
A Guided Journey from Foundations to Research
30 topics spanning LLM foundations, prompt engineering, RAG systems, LangChain agents, and multimodal models.
Hands-on LLMs: Series Intro
- Overview of LLMs, applications, course structure
The LLM Evolutionary Tree
- History, milestones, architecture evolution
Running Microsoft Phi-3 using Hugging Face
- Practical deployment of Microsoft Phi-3 model locally using Hugging Face APIs
Fine-tune BERT (Sentiment)
- Hands-on session on fine-tuning BERT to perform sentiment analysis
Flan-T5 for Classification
- Prompt formatting, generative vs discriminative
Using ChatGPT API for Movie Review Classification
- Utilize OpenAI's ChatGPT API to build a practical sentiment classification pipeline
Text Clustering using Sentence-Transformers
- Implementing text clustering techniques on ArXiv research papers
Topic Modeling with BERTopic
- Hands-on project applying BERTopic to identify themes from ArXiv research papers
LLMs for Text Clustering and Topic Modeling
- Identification on textual datasets, LLM-assisted clustering
Intro to Prompt Engineering
- Fundamentals of crafting effective prompts for maximizing LLM outputs
Advanced Prompt Engineering
- Deep dive into in-context learning, Chain-of-Thought, and Tree-of-Thought methods
LLM Guardrails
- Techniques to set constraints, safety measures
LangChain & Agents (Intro)
- Intro to building complex LLM applications and Agents using LangChain
LLM Quantization
- Understanding model quantization methods for efficient deployments of LLMs
Coding Chains (LangChain)
- Hands on demonstration of creating coding chains using LangChain
How to give Memory to LLMs
- Techniques for implementing short-term and long-term memory in LLM applications
Code your First LLM Agent using LangChain
- Step-by-step project to build a functioning LLM-powered agent
Semantic Search & RAG
- Basics of semantic search and RAG concepts
Coding an LLM Dense Retrieval System
- Build a practical dense retrieval system using LLM embeddings
Chunking Strategies for LLM
- Effective strategies for breaking down text into meaningful chunks for retrieval and processing
Reranking for Semantic Search
- Understand and implement reranking methods
Evaluating Retrieval Systems
- Measure retrieval effectiveness using MAP and nDCG
RAG: Intro & Coding
- Hands-on implementation of basic RAG systems
Advanced RAG
- Explore and apply advanced methodologies in RAG for improving accuracy and context management
Evaluating RAG Systems
- Best practices and metrics for assessing the performance of RAGs
Vision Transformers: How and Why They Work?
- Understand ViTs, how they differ from CNNs
Intro to CLIP
- Explore how CLIP bridges vision and language
Intro to BLIP
- Learn how BLIP enhances text generation
Multimodal LLMs: 30-Minute Summary
- A concise overview of Multimodal LLMs
Series Summary
- Reviewing the concepts covered, key takeaways
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
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
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.
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.
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.
Manning #1 Best-Seller
Build a DeepSeek Model (From Scratch)
By Dr. Raj Dandekar, Dr. Rajat Dandekar, Dr. Sreedath Panat & Naman Dwivedi
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.
Sample Papers From Our Research
A selected few papers from our research over the past years. Students in the Industry Professional plan work on similar projects aimed at publication.
Enroll in the Bootcamp
Choose the plan that matches your goals, from self-paced learning to intensive research mentorship with MIT PhDs.
Student Plan
Save 25%. Originally Rs 40,000.
- Lifetime access to all videos, code files, and homework assignments
Industry Professional
Save 17%. Originally Rs 1,50,000. MIT and Purdue PhDs as your research mentors.
- 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
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.