How a RAG Application Works

 Here’s what happens inside a typical rag application:

User → Question → Retriever → External Data → LLM (Generator) → Final Response

Let’s break it down:

  • The user asks a question.
  • A retriever searches internal or external content (PDFs, databases, websites).
  • The most relevant piece of data is pulled in.
  • That content is passed into the LLM.
  • The model uses it to generate a fact-aware response.

That’s how the best rag examples deliver sharp, grounded answers—without training or manual tuning.

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