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  "path": "/tienbku/rag-vs-agentic-rag-vs-graph-rag-which-one-actually-fits-your-use-case-19ho",
  "publishedAt": "2026-07-02T01:41:03.000Z",
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    "RAG vs. Agentic RAG vs. Graph RAG: Which One Actually Fits Your Use Case? - BezKoder"
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  "textContent": "If you’ve built anything with LLMs in the last couple of years, you’ve built a RAG pipeline. Embed the query, search a vector store, stuff the top chunks into a prompt, let the model talk. It’s the “Hello World” of grounding LLMs in real data – and for a long time, it was enough.\n\nIt isn’t anymore.\n\nThe moment your use case involves multi-hop reasoning, tool calls, or relationships between entities scattered across thousands of documents, naive RAG starts cracking. That’s given rise to two evolutions worth understanding deeply: Agentic RAG and Graph RAG. They solve different problems, and confusing them will cost you weeks of rebuilding. Let’s walk through all three, step by step.\n\n\n\n\n\n##  \n            RAG vs. Agentic RAG vs. Graph RAG: Which One Actually Fits Your Use Case? - BezKoder\n          \n\nA breakdown of three retrieval architectures: RAG, Agentic RAG, Graph RAG - why \"just add RAG\" stopped being good enough advice a while ago\n\nbezkoder.com",
  "title": "RAG vs. Agentic RAG vs. Graph RAG: Which One Actually Fits Your Use Case?"
}