Glossary
Retrieval-Augmented Generation (RAG)
RAG retrieves relevant material from the organization’s own sources and gives it to the language model before the model answers. In distribution, that is how a reply about a SKU, invoice, or campaign is tied to real records instead of the model’s general training.
Also called بازیابی افزودهٔ تولید
Without this method, the model answers only from the general knowledge of its training period and knows nothing about your company’s invoices, SKUs, or campaigns. With it, the model acts as a reasoning layer on top of real data.
An important limit: this method is suitable for text, not for precise numeric calculation. For questions such as “what were this month’s sales,” the right method is a specific query on the database, not semantic retrieval. Well-designed systems have both paths and choose by question type.
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