The problem RAG solves
General-purpose AI models know a lot about the world but nothing about your policies, contracts, products or customers. Asked about them, they may guess. Enterprises need answers that are specific, current and traceable to a source.
How RAG works
- Your documents are processed and indexed so they can be searched by meaning
- When a user asks a question, the system retrieves the most relevant passages
- Those passages are given to a language model as context
- The model generates an answer grounded in the retrieved content, and can point back to the sources
Why enterprises use RAG
- Answers grounded in your own sources
- Up to date without retraining a model
- Traceability back to the original document
- Access to knowledge that is otherwise buried in long documents
Where RAG fits
RAG is most valuable where knowledge lives in documents: policies and circulars in banks, contracts in legal teams, manuals in operations, and records in public institutions.
Polisacs builds RAG into Nyodoc, our AI-powered document management system, alongside intelligent search, summarization and information extraction.
Frequently asked questions
Is RAG the same as training a model on our data?
No. RAG retrieves relevant content at the time of the question and supplies it to the model, rather than retraining the model on your data.
Does RAG reduce AI hallucinations?
Grounding answers in retrieved sources helps keep responses tied to your content and makes them easier to verify, though answers should still be reviewed for critical decisions.