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What does RAG stand for?
RAG stands for Retrieval-Augmented Generation. The system first retrieves relevant information from a knowledge base, document set or another source. Only then does the model use that context to build an answer.
Technology - 6 min
What is RAG?
RAG is a way to let a language model retrieve relevant information from documents or a knowledge base before it answers. That usually leads to answers that are more specific, grounded and useful for your own organization.
5 sections Practical explanation For teams and organizations
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What happens technically?
In a RAG setup, a question is first translated into a retrieval step. Relevant text fragments are then selected and sent along with the prompt. The model answers not only from its original training, but also from material retrieved at that moment.
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Why is this valuable?
A standard LLM does not automatically know what is inside your manuals, policies or project documents. With RAG, the system can answer based on your own information. That makes AI much more useful for internal knowledge questions, document support and customer-facing processes.
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What determines quality?
Quality depends on source data, document structure, retrieval logic and whether the right passages are selected. Poorly structured or outdated documents will still produce weak results. RAG is therefore not magic, but a chain that has to be designed carefully.
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When is RAG not enough?
RAG helps with finding knowledge, but it does not solve everything. For complex workflows, decision rules, permission handling or system actions, you often need additional layers such as validation, human review or agent logic. RAG is often a strong foundation, not the whole solution.
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What is an LLM?
An LLM is a large language model that can understand, predict and generate text. It often feels smart, but the quality of the result still depends heavily on context, model choice and how you use it.
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What is edge AI?
Edge AI means AI runs close to the source of the data, for example on a device, server or local network. This can improve speed and often gives more control over privacy, continuity and cost.
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What is context in AI?
Context is all the extra information you give a model so it can better understand what you mean. Good context is often the difference between a generic answer and one that actually fits your task, organization or document.
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