Enterprise AI
RAG is not a product.
It is possible to chunk documents, generate embeddings, search a vector database and send the retrieved context to a model. Making this flow work matters. But what you have at that point is not yet a product.
A technical flow is not a user outcome
RAG answers whether relevant information can be retrieved and supplied to a model. The user asks whether they can reach the right information, with the right permission, and trust it enough to make a decision.
The gap is defined by product choices: scope, freshness, refusal behaviour and the feedback path when the system is wrong.
Enterprise knowledge is more than content
Access to a document does not mean every fragment should be visible to everyone. Permissions may be required at user, group, source or sensitive-section level.
Security is therefore not a filter added at the end; it is a starting condition of the product experience.
Attribution is only the beginning of trust
Users should see which source and passage support an answer. Attribution, however, is not a guarantee of truth. Stale, conflicting or ownerless content can still lead to a wrong decision even when it is retrieved correctly.
A RAG initiative is also a knowledge ownership and content-lifecycle initiative.
The product begins after the answer
A technical flow starts becoming a product when quality evaluation, feedback, access policy, cost, latency, monitoring and operational ownership become visible.
The first question should therefore not be which vector database to use, but whose decision we are helping make more reliable.