What is RAG? Grounded answers on your docs

RAG means retrieval-augmented generation: the system finds relevant passages from your documents or systems, then asks a model to answer using that context — ideally with citations. It is how you get assistants that know your policies without fine-tuning a new model every month.

Commercial shape on this site: RAG & knowledge assistants →.

What buyers get

Answers grounded in approved sources instead of generic web knowledge.
Permission-aware retrieval so people only see what they are allowed to see.
Clearer escalation when confidence is low or sources conflict.
A path to agents later — when answers must become actions in tools.

When RAG fits

Internal SOP and policy Q&A, staff assistants that cut ticket ping-pong, and customer help on approved content. It pairs with document and API integrations → and sometimes automation → for handoffs.

If the job is multi-step work across systems, RAG alone is not enough — see agent vs chatbot → and agents →.

Pitfalls

Dumping messy drives into an index with no chunking, metadata or ownership.
No citations — users cannot verify claims.
Ignoring permissions so retrieval leaks across teams.
Expecting RAG to invent process fixes; it retrieves, it does not redesign ops.

How we usually start

Pick one corpus and one audience. Define “good answer” with sample questions. Decide citation and escalation rules before polishing the UI. Audit helps if you have many candidate corpora; Scope if the corpus and success tests are already clear.

Related guides & services

Want assistants on your documents?

We design retrieval, permissions and citations for production — then connect to agents when answers must become actions.

RAG & knowledge assistants Contact Aideveloper