Knowledge & Data Systems

RAG is how you make an AI answer questions from your own documents instead of from its general training data. The "retrieval" part: when you ask a question, the system first searches your documents and pulls out the most relevant passages. The "generation" part: those passages get handed to the AI model along with your question, so its answer is grounded in your actual content. The reason this matters is twofold. First, the AI stops making things up about your business, because it has the real text to read. Second, you can update the source material any time and the AI answers update with it. No retraining required.

Yes, if you choose your architecture carefully. The leak risk comes from one specific place: uploading sensitive documents to a public AI service whose terms of service let them use your data for training, or whose servers sit in a jurisdiction you'd rather avoid. There's nothing about searching with AI that inherently leaks data. A privately hosted RAG system, running on EU infrastructure with a clear no-training contract, can be safer than the file server most companies already use. The shift from risky to safe is mostly a matter of which model you use, where it runs, and what the contract says.

Probably not as the first step. Vector databases are the standard answer in the AI engineering world, and for systems that handle millions of documents and thousands of users, they're the right answer. But for a small organization with a few hundred documents, a simpler setup works fine and costs nothing to run. Sometimes the right answer is a folder structure plus search. Sometimes it's a basic embedding index that lives on a single server. We pick what fits the scale, not what looks impressive on a technical diagram. You can always graduate to a vector database later if your volume grows.

Three things. ChatGPT's free tier doesn't keep your documents available between sessions, so you'd be re-uploading every time. Its context window is also limited, which means longer documents get truncated or summarized before the model sees them. And by default, your uploads can be used to train the model unless you've explicitly opted out. A properly built RAG system keeps your documents indexed and available all the time, handles documents of any length by retrieving only the relevant parts, and runs in an environment where the no-training rule is contractual. ChatGPT uploads are good for quick one-offs. A properly built knowledge system is a different category of tool.