Mastering Generative AI - 1 Day Workshop at IIT Bombay

1 Day Workshop on Mastering Generative AI

Duration: 1 Day | 8 Hours of Training
Eligibility: Engineering, biomedical and other students, researchers, and working professionals interested in AI. Basic Python familiarity is recommended.

Generative AI Foundations & Applications
1. Introduction to Generative & Agentic AI
Introduction to AI, Machine Learning, Deep Learning and Generative AI.
Differences between Generative AI, AI assistants and AI agents.
Applications across education, engineering, healthcare and business.
Overview of language, image and multimodal models.

2. Transformers & Large Language Models
Conceptual introduction to transformers and attention.
Tokens, embeddings and context windows.
Overview of GPT, Claude, Gemini and open-weight models.
Introduction to pre-trained models and Hugging Face.
Practical: Compare model responses using different prompts.

3. Prompt Engineering & Structured Outputs
Write effective prompts using instructions, context and examples.
Use system instructions and reusable prompt templates.
Generate structured JSON responses.
Understand hallucinations and basic output validation.
Hands-on: Create prompts for information extraction and summarization.

4. Building LLM Applications
Connect a Python application to an LLM API.
Introduction to LangChain and prompt templates.
Manage conversation history and context.
Understand API keys, token usage and basic cost controls.
Hands-on: Build a simple conversational AI assistant using a starter project.

5. Retrieval Augmented Generation (RAG)
Understand RAG and when to use it.
Load PDF documents and split them into chunks.
Generate embeddings and understand vector similarity search.
Introduction to FAISS or Chroma.
Retrieve relevant information and generate document-grounded answers.
Include source references and handle unanswered questions.
Hands-on: Build a PDF question-answering assistant using a prepared starter project.

6. Model Adaptation & Responsible AI
When to use prompting, RAG or fine-tuning.
Conceptual introduction to Parameter-Efficient Fine-Tuning (PEFT).
Data privacy, bias, hallucinations and responsible AI use.
Discuss when model adaptation is justified in real-world projects.

Understand LLM fundamentals and build a conversational assistant and a document-based RAG application.