RAG vs general AI chat: when you need your own knowledge base
How RAG differs from a plain AI chat, when a chat is enough, and when business needs a corporate knowledge base with sources and permissions.
A general AI chat answers from the model’s general knowledge. RAG first finds your documents — and only then forms the answer.
In short: the difference
A general AI chat relies on what the model learned, plus text you paste into the chat. It does not know your Confluence until you copy a fragment in yourself.
RAG (Retrieval-Augmented Generation) searches your knowledge base for relevant chunks before answering and passes them to the model as context. That is why the answer can be tied to a specific document and version.
When a general AI chat is enough
If the question is general, the data is not sensitive, and you do not need to verify the answer against an internal policy, a chat is usually enough.
- Drafting an email or translation
- Ideas for a presentation without corporate numbers
- One-off questions where a mistake is not critical
When you need your own knowledge base
You need your own knowledge base when the right answer lives inside the company: in policies, tickets, contracts, runbooks, email threads.
- An employee must get an answer from the current document version
- You need source links for verification and audit
- Different roles must see different content
- Data cannot simply be fed into an external chat
What matters in enterprise RAG
Answer quality depends on more than the model. Chunking, index freshness, access rights, quality evaluation and a clear UX with citations all matter.
That is why “we connected an LLM” and “we got a working corporate knowledge base” are different projects. Gleyk is the second one: knowledge, permissions and scenarios on the model of your choice.