RAG explained without jargon: how AI looks up your data

The technique behind many corporate chatbots is simple to understand, and it has limits worth knowing.

A language model does not know your company's contracts, manuals, or policies. RAG, short for retrieval-augmented generation, is a way to hand it the right passages at the moment a question is asked.

The three steps

• Documents are split into chunks and indexed.
• For each question, the system retrieves the most relevant chunks.
• The model receives those chunks and writes its answer based on them.

Where these systems fail

In practice, many errors happen during retrieval, before the model writes anything. If the right passage is not found, the answer can be well written and still wrong. Outdated documents and poorly extracted tables are among the most common causes.

That is why it pays to test the system with questions whose answers you already know and to show sources to users. Switching models rarely fixes bad retrieval.

Educational content only. Nothing here is investment advice.