How to Build a Custom RAG Application: A Non-Technical Founder's Guide

The AI Hallucination Problem

If you ask ChatGPT about your company's HR policy, it will lie to you (hallucinate) because it hasn't read your internal PDFs.

Enter RAG

Retrieval-Augmented Generation (RAG) fixes this. It is the architecture of fetching relevant documents from a database *before* asking the AI a question.

How We Build It

1. Ingestion: We take your thousands of PDFs, parse the text, and chunk it into paragraphs.
2. Embedding: We convert those paragraphs into numbers (vectors) using an embedding model.
3. Storage: We store these vectors in a specialized Vector Database like Pinecone.
4. Retrieval: When a user asks a question, we mathematically search the Vector Database for the most relevant paragraph, attach it to the prompt, and send it to OpenAI to generate an accurate answer.

RAG is how you build an enterprise-grade AI moat. Let Oktuv build yours.