blog/ai industry
What Is RAG? How AI Answers Questions From Your Own Company Data
SoftiIntel Team · · 7 min read
the short answer
RAG (retrieval-augmented generation) is a technique where an AI first searches your own documents for the passages relevant to a question, then writes its answer using only those passages — ideally citing them. It lets a general AI model answer accurately about your business without retraining it, and makes answers checkable.
Why plain AI models get your business wrong
A general model knows the public internet up to its training date, not your prices, policies or contracts. Asked about them, it may guess. RAG fixes this by handing the model the right facts at question time.
How RAG works, step by step
- Your documents are split into passages and indexed for meaning (embeddings).
- A question comes in; the system retrieves the most relevant passages.
- The model writes an answer using only those passages.
- The answer cites its sources so people can verify it.
Where businesses use RAG
- Website and WhatsApp chatbots that answer from your real policies.
- Internal assistants over SOPs, HR policies and product manuals.
- Sales assistants that know every spec sheet and price list.
- Support agents that draft replies from past resolved tickets.
RAG vs fine-tuning
Fine-tuning changes how a model behaves or writes; RAG changes what it knows at answer time. For keeping answers current with changing documents, RAG is usually cheaper and easier. Read more in LLM APIs vs fine-tuning vs RAG.
What makes RAG good or bad
- Clean, up-to-date source documents.
- Sensible chunking and good retrieval.
- Instructions to say “I don't know” when sources don't cover it.
- Access control so people only retrieve what they're allowed to see.
Build one
We build RAG assistants for websites, WhatsApp and internal teams — see AI integrations and our AI chatbot for website.