RAG (Retrieval Augmented Generation) (التوليد المعزز بالاسترجاع)
Retrieval Augmented Generation (RAG) is an AI architecture that grounds a language model in a company's own data. Before answering, it retrieves the most relevant documents from a knowledge base, then generates a response based on that real content — reducing hallucinations.
In Iraqi dialect
يخلّي الذكاء الاصطناعي يگرا من ملفاتك قبل ما يجاوب
In detail
A language model alone knows only what it learned during training and cannot see a company's private files or live information. RAG fixes this by adding a retrieval step: the user's question is converted into a vector and matched against an indexed knowledge base, and the top relevant passages are injected into the model's prompt. The model then answers from those passages rather than from memory. This makes responses accurate, current, and citable, and lets businesses deploy AI over policies, product catalogs, and support articles without retraining the model. RAG is the backbone of most enterprise chatbots and AI knowledge assistants.
Practical example
A bank's assistant uses RAG to answer policy questions by pulling from its internal compliance documents, not the open web.
Frequently asked questions
Why use RAG instead of just an LLM?
RAG grounds answers in your real, current data, cutting hallucinations and letting the AI cite actual sources.
Does RAG require retraining the model?
No — it retrieves and injects relevant content at query time, so you update the knowledge base instead of the model.
Related terms
Related services
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