PromptSharp › Glossary › Retrieval-Augmented Generation (RAG)
What is Retrieval-Augmented Generation (RAG)?
RAG feeds the model relevant documents at query time so its answer is grounded in your source data, not just its training.
Retrieval-augmented generation (RAG) is a technique where, before answering, the system fetches relevant passages from your documents or knowledge base and hands them to the model as context. The model then answers from that material instead of relying only on what it memorized in training.
RAG is how AI assistants answer questions about your private files, recent events, or a specific handbook accurately. It reduces hallucination and keeps answers current, but it depends on retrieving the right passages — poor retrieval yields poor answers even with a great model. For a lightweight version, you can paste the source into the prompt yourself and instruct the model to answer only from it.
Frequently asked
What is RAG in simple terms?
RAG means the AI looks up relevant information from your documents first, then answers using that information. It's how a chatbot can accurately answer questions about your company's files instead of guessing from general training.
Is RAG the same as fine-tuning?
No. RAG supplies fresh facts at question time without changing the model. Fine-tuning permanently adjusts the model's weights to shift its style or behavior. RAG is usually the cheaper, more current way to add knowledge.
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