One-Day Workshop on Mastering Generative AI

Duration: 1 Day | 8 Hours of Training

Eligibility: Students from engineering, biomedical, and other disciplines, 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.

Learning Outcomes

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