Planning New Tutorial Series To Cover End to End Working Own AI-Powered ...

New Tutorial Series To Build Your Own AI-Powered PDF Q&A App (RAG System) Without Being Expert in AI Concepts

YouTube tutorial series to build a RAG system with PDF document support using Streamlit, Pinecone, LangChain, and Gemini. 

The structure assumes no technical background and guides viewers step-by-step toward building a working AI-powered app 

by the end.

Part 1: What is RAG? (And Why It’s Awesome for PDF Q&A)

• Explain RAG in simple terms using real-life examples (like a smart librarian).

• Use diagrams to show how data moves from PDF → Vector DB → LLM.

• Demo the final app to build excitement.

• Tools overview: Streamlit, Pinecone, LangChain, Google GenAI Models API

Part 2: Project Setup – Tools You’ll Need

• Installing Python, setting up virtual environment

• Installing required packages (pip install streamlit pinecone-client langchain ...)

• Setting up .env file for Pinecone and Google Gemini API keys

• Folder structure walkthrough (simple explanation)

Part 3: Upload & Process PDF Files (User-Friendly)

• ✨ Use the rag_app_v2.py interface to upload files

• Where PDFs are stored

• What happens behind the scenes when PDFs are uploaded

• What “chunking” means and why it's needed (explained simply)

Part 4: Embedding PDFs into Pinecone (No Hardcoding!)

• Introduction to Vector Embeddings (with visuals)

• Explain how LangChain + Google Gemini turns your docs into AI-searchable chunks

• Step through add_new_documents_to_index() logic

• Easy breakdown of chunking logic and uploading to Pinecone

Part 5: Build the Brain – Creating the RAG Chain

• What is a “RAG Chain”?

• Dissecting create_rag_chain() step-by-step

• Gemini model, prompt template, retriever—how they talk to each other

• Simple walkthrough of how the app answers questions from indexed PDFs

Part 6: Building the Frontend with Streamlit

• Walkthrough of rag_app_v2.py interface

• Customizing layout and inputs for non-tech users

• Real-time querying and answering—live test

Part 7: Deploy Your AI PDF Chat App (FREE!)

• Using platforms like Streamlit Cloud or Render

• Managing environment variables safely

• Share your app with the world!

Bonus Part: Improving Answer Quality & Debugging Tips

• Tweak prompt template for better answers

• Common errors and how to fix them

• How to monitor usage and performance

By the end of the series, viewers will be able to:

✅ Upload any PDF 

✅ Search and chat with its content using AI 

✅ Understand what happens behind the scenes 

✅ Deploy their own custom Q&A app