It supplies the model with relevant external information: What does RAG primarily do?
RAG retrieves relevant external information at query time to ground the model's responses in current, specific content.
The question
Among the deployment options for improving a foundation model's fit, a team considers retrieval augmented generation (RAG). What does RAG primarily do?
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- It supplies the model with relevant external information retrieved at query time so responses draw on current, organization-specific content. ✓Correct because RAG augments generation by retrieving external, up-to-date context to ground the model's outputs at inference time.
- It permanently rewrites the model's internal weights using new labeled examples so the added knowledge is baked into the parameters themselves.Plausible because it describes fine-tuning, a different technique that changes weights rather than retrieving external context.
- It compresses the model into a smaller footprint so it can run on edge devices with limited memory and processing capacity.Plausible as an optimization, but model compression/distillation is unrelated to retrieving external information for generation.
- It chains multiple autonomous agents together so the system can plan and execute multi-step tasks without any direct human help.Plausible because it describes agentic architectures, another listed technique, not the retrieval mechanism that defines RAG.
The trap
Confusing retrieval augmented generation with fine-tuning, which changes the model's weights. How to remember it
RAG retrieves relevant external information at query time to ground the model's responses in current, specific content.
How many of these would you get right?
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