Have you ever asked an AI about something that happened this morning, or about a private document on your laptop, and it hallucinated an answer? That's because LLMs have a "cutoff date." RAG fixes this. Instead of trying to memorize the whole world, the AI "looks up" the relevant information in your documents before it answers. Today, we’ll build the foundation of a RAG pipeline. 🏗️ The 5 Steps of RAG To give an AI a library, we follow a simple assembly line: 1. Load: Pulling data from a PDF, Website, or Text file. 2. Split: Breaking long documents into small, bite-sized "chunks." 3. Embed: Converting those text chunks into numbers (vectors) that represent their meaning. 4. Store: Saving those numbers in a "Vector Database." 5. Retrieve: Finding the right chunk when a user asks a question. 🛠️ Step 1: Loading & Splitting AI can't read a 50-page PDF all at once. We have to "chunk" it so the AI only reads the relevant parts.…