RAG stands for Retrieval-Augmented Generation. It solves the core problem of generic language models: they know nothing about your specific business and may invent answers.
How does RAG work, step by step?
- The user asks a question.
- The system finds the passages in your knowledge base related to the question.
- Those passages are passed to the language model together with the question.
- The model writes the answer from that approved knowledge and can cite the source.
Why are the answers more reliable?
Because the model doesn’t answer “from memory”; it answers from your documents. If the information isn’t in the knowledge base, a well-designed chatbot honestly hands the user to a human instead of guessing.
What do you need to build one?
- Your documents, prices and frequently asked questions
- Answering rules and brand tone
- A clear path for human hand-off
- Where it runs: website, Telegram, WhatsApp or an internal panel
An important note for Persian-language content
Search quality on Persian text depends on normalising characters (Arabic vs Persian ye and kaf, zero-width non-joiners) and choosing a suitable embedding model. Before launch, test the chatbot with real, colloquial Persian customer questions.
Frequently asked questions
Does RAG replace support staff?
No. RAG answers the frequent questions so staff can focus on complex cases, and it escalates to a human when information is missing.
What happens when company knowledge changes?
You update the knowledge base and the chatbot answers from the new version; there’s no need to retrain the model.