Basic Gen AI for college students

🧠 What You'll Learn in This Session

1. 🔍 Text Summarization with LLMs
2. Learn how to build a LangChain-powered summarizer that condenses long documents or articles into concise, meaningful summaries using prompt templates and chains.
3. ❓ Question Answering over Custom Data
4. Build a QA system that answers user queries based on uploaded files (CSV, PDF, etc.) using embeddings, vector stores (like FAISS), and LLM reasoning.
5. 📄 Key Information Extraction from Invoices
6. Create an AI agent that extracts structured data (like invoice number, date, total amount) from unstructured invoice PDFs using LangChain and OCR tools.
7. 🧠 Understanding LangChain Components
8. Get hands-on with LangChain’s core modules—Chains, Agents, Tools, Prompts, and Memory—and understand how they work together to build intelligent apps.
9. 🚀 Deploying Your GenAI App with Streamlit
10. Learn how to wrap your LangChain workflows into a user-friendly Streamlit interface for real-time interaction and sharing.