In short: What is RAG? Retrieval-augmented generation fetches the most relevant passages from your own documents and records, so an AI model answers from them and cites them instead of guessing.
Large language models are fluent, but on their own they do not know your prices, policies or stock levels — and they can confidently make things up. Retrieval-augmented generation (RAG) fixes that.
How RAG works
- Ingest: your SOPs, contracts, catalogues and ERP records are split into passages and indexed.
- Retrieve: when someone asks a question, the most relevant passages are fetched — respecting that user's permissions.
- Generate: the model answers using only those passages and cites them.
Where it pays off
- Customer support on WhatsApp that quotes your real return policy.
- Sales staff asking "what is the stock of item X in Lahore?" in plain language.
- Finance teams searching contracts and invoices instantly.
From answers to actions
Add agents with scoped tools and the assistant can create a quotation, post an entry or send a reminder — under approval gates and with a full audit trail.
Explore our AI services or start a four-week pilot on one process.
More about what is rag
Want to go further with what is rag? These Netstech guides and services are a good next step:
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