A model knows only what it was trained on. It has never seen your contracts, your manuals, or last week’s prices. Ask about them and it either refuses or guesses.
The fix is simple: before the model answers, look up the relevant pages, then hand them to the model together with the question.
Three Steps
First, retrieve: search your documents for the passages closest to the question. Second, augment: add those passages to the request. Third, generate: the model writes its answer using what it was just given.
An Example
An employee asks, how many days of leave do I get for a new child? The system finds the right page of the staff handbook, the model reads it, and answers with the exact number.
That is retrieval-augmented generation, usually shortened to RAG.
Why It Is Popular, and Its Weak Point
It is popular for good reasons. The knowledge stays current, because you update the documents, not the model. The answer can show its sources. And private information stays in your own systems.
But it has one weak point: the answer is only as good as what was retrieved. If the search brings back the wrong page, the model answers confidently from the wrong page.
The Business Engineer framework maps where each part of the AI stack sits and who holds the leverage. These pills are short lessons from the same library.
Gennaro is the creator of FourWeekMBA, which reached about four million business people, comprising C-level executives, investors, analysts, product managers, and aspiring digital entrepreneurs in 2022 alone | He is also Director of Sales for a high-tech scaleup in the AI Industry | In 2012, Gennaro earned an International MBA with emphasis on Corporate Finance and Business Strategy.
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