Using RAG with your company data

RAG (Retrieval-augmented generation) is what allows you to use large language models for specific business use cases.

It’s a great way for organizations to use AI WITHOUT giving away the keys to the kingdom.

Instead of training a model with sensitive company data, RAG lets a large language model query directly against it, providing what the industry calls “grounded” responses without risking your data’s security.

Let’s use a simple RAG app as an example.

When you’re building a specialized RAG app, you’re taking a general Large Language Model (think ChatGPT) and focusing it on specific tasks or knowledge.

By exposing specific data—like documents or internal files—that data gets stored in a vector database, and your LLM queries against this database for the personalized knowledge without training the LLM.

You get tailored, business-specific responses—no excess, no leaks.

Have you had a chance to do a simple RAG proof of concept? I’d love to hear your use case in the comments below!

Видео Using RAG with your company data канала Schema Sauce