The knowledge exists. Finding it takes time.
The current procedure lives in one archive, its appendix in another, and the practical explanation with a colleague. An enterprise knowledge assistant connects questions to approved sources rather than relying on a model’s general knowledge alone.
Retrieval with context and boundaries
With retrieval-augmented generation, or RAG, the system first checks which documents a person may access. It then retrieves relevant passages for the model. Answers carry source references and version context, while making missing evidence visible.
- Assign an owner and review date to each source.
- Check permissions before search and retrieval.
- Decline when reliable supporting information is missing.
Measure the useful work
A pilot compares time to find information, citation accuracy and the share of questions answered usefully. Universal accuracy is not a credible promise: conflicting and outdated documents require editorial ownership.
A practical starting point
Choose one procedure library and questions people actually ask. An illustrative workflow is finding the right purchase approval process with a reference to the active policy. Expand the source collection after validation.
The information flow
- Question
- Permitted sources
- Context
- Cited answer
Models and documents run on your infrastructure. Network policies determine permitted connections.
Practical questions
Does the model train on all our documents?
It does not need to. RAG retrieves relevant context for each request. Fine-tuning is a separate choice with different requirements and risks.
What happens when sources conflict?
The system should surface the discrepancy and source versions. An assigned content owner then decides which document is authoritative.