Every growing company accumulates documents: HR policies, product manuals, pricing sheets, SOPs, client FAQs. New employees ask the same questions again and again, and experienced staff spend time answering them. A retrieval-augmented generation (RAG) assistant solves this by letting people ask questions in plain language and get answers drawn from your own documents.
How RAG works
- Your documents are split into small passages and stored in a searchable index.
- When someone asks a question, the system finds the most relevant passages.
- An AI model writes an answer using only those passages, and cites where each fact came from.
Because the answer is based on retrieved text, you can update knowledge simply by updating the documents.
Preparing your content
The assistant is only as good as the documents behind it. Before building:
- Remove outdated and duplicate versions.
- Give each document a clear title and owner.
- Convert scanned PDFs into searchable text.
- Mark documents that only certain roles should see.
Permissions matter
An HR assistant should not show salary policies to everyone. Retrieval must respect user roles so people only receive answers from documents they are allowed to access.
Test with real questions
Collect 50–100 genuine questions from staff along with the correct answers. Use them to measure accuracy before launch and after every change.
Good first use cases
- HR and leave policy questions
- Product specification lookups for sales teams
- Support agents searching troubleshooting guides
- Onboarding new employees
FAQs
Can it work in Hindi?
Yes. Staff can ask in Hindi or English even if documents are in English.
Where is the data stored?
In a vector database on your chosen cloud, ideally an Indian region, with access controls.
What if the answer is not in the documents?
The assistant should say it does not know and suggest who to contact.
