RAG is not the product

RAG has become shorthand for "AI on our data". But retrieval-augmented generation is a technique, not a product. Customers do not buy RAG; they buy faster answers, fewer errors and less manual work.

Start from the question

What questions do users actually ask? What does a good answer look like? What happens when the answer is wrong? These product questions should shape the retrieval design, not the other way round.

Quality lives in the data

Most RAG failures are content failures: outdated documents, poor structure, missing permissions. Data ownership and freshness are product decisions.

Evaluate the experience

Measure answer accuracy, citation quality, refusal behaviour and time saved. A technically elegant pipeline that users do not trust is not a product.