Build a Real AI App 💕

Build a Real AI App — Project-Based LLM Notes

1. Project Setup: npm init, package.json, and folder structure from scratch.
2. How AI Reads PDFs: The real pipeline behind ChatGPT-style apps — why AI can't read files directly.
3. Express Backend: Build your first server — routes, requests, responses, and middleware explained simply.
4. File Upload: Handle PDF uploads with Multer and understand multipart/form-data.
5. PDF Text Extraction: Extract text using pdf-parse, understand buffers and real version fixes.
6. Chunking: Split large text into meaningful pieces — fixed-size vs paragraph-based.
7. RAG: The architecture behind ChatGPT, Perplexity, and every serious AI app — explained simply.
8. Embeddings: How text becomes numbers, and why semantic search beats keyword search.
9. Gemini Integration: Set up free Google Gemini API — generation model vs embedding model.
10. Semantic Search: Cosine similarity from scratch to find the most relevant chunk.
11. Full RAG Pipeline: PDF → chunks → embeddings → retrieval → Gemini answer — end to end.

Why Buy This:

1. Project-Based: Build a real working AI app, not just theory.
2. Beginner Friendly: Simple language, real-world analogies throughout.
3. Free Stack: Google Gemini free tier — no OpenAI billing needed.
4. Real Debugging: Actual errors with fixes included.
5. Career Relevant: RAG and embeddings are core AI engineering skills today.

These are notes from a real learning session — built step by step. Your starting point for understanding how AI apps actually work.

Start building. Not tomorrow. Now.