Intro to RAG (Chat with Your Docs)

Written by Jakub Rusinowski · Last updated July 10, 2026

Retrieval Augmented Generation (RAG) allows an LLM to answer questions based on your own private data (PDFs, Notes, Emails).

In This Guide

• How it Works
• Why use RAG?

Retrieval Augmented Generation (RAG) allows an LLM to answer questions based on your own private data (PDFs, Notes, Emails).

How it Works

1. Ingestion: Your text documents are chopped into small chunks. 2. Embedding: Each chunk is converted into a vector (a list of numbers) representing its meaning. 3. Retrieval: When you ask a question, the system finds the most similar chunks. 4. Generation: The chunks are sent to the LLM along with your question.

Why use RAG?

• Accuracy: Reduces hallucinations by grounding the AI in facts.
• Privacy: Your data stays local.
• Context: The AI knows things it wasn't trained on (e.g., your specific project details).

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