Grounding AI in Your Own Knowledge (RAG)
Why models invent answers about your business, and how retrieval-augmented generation fixes it by giving them your actual sources.
Citations, refusal, and evaluating answers
Grounded is not the same as correct RAG dramatically reduces invention, but it does not eliminate it. A model can still misread a passage, blend two sources, or answer confidently from a chunk that was retrieved but irrelevant. So you check. Three instructions that do most of the work Answer only from the provided sources. […]
Measuring a RAG system honestly
“It seems good in the demo” is where most RAG projects stop Grounded systems demo beautifully. Someone asks three questions they already know the answers to, the citations look convincing, and the project is declared a success. Then it meets real questions from people who cannot check the answers. Because RAG has two components, it […]
When not to build RAG
The most expensive RAG system is the one that should have been a search box RAG is genuinely the right answer for a large body of documents and open-ended questions. It is also proposed constantly for problems it does not fit, at considerable cost. Four cases where something simpler wins The corpus is small and […]