Build an Agentic RAG Pipeline with Llama 3.1 and NVIDIA NeMo Retriever NIMs – NVIDIA Technical Blog News and tutorials for developers, data scientists, and IT admins 2025-07-26T02:39:18Z http://www.open-lab.net/blog/feed/ Vinay Bagade <![CDATA[Build an Agentic RAG Pipeline with Llama 3.1 and NVIDIA NeMo Retriever NIMs]]> http://www.open-lab.net/blog/?p=85884 2024-10-28T21:47:15Z 2024-07-23T15:15:00Z Employing retrieval-augmented generation (RAG) is an effective strategy for ensuring large language model (LLM) responses are up-to-date and not...]]> Employing retrieval-augmented generation (RAG) is an effective strategy for ensuring large language model (LLM) responses are up-to-date and not...An illustrations representing agnetic RAG.

Employing retrieval-augmented generation (RAG) is an effective strategy for ensuring large language model (LLM) responses are up-to-date and not hallucinated. While various retrieval strategies can improve the recall of documents for generation, there is no one-size-fits-all approach. The retrieval pipeline depends on your data, from hyperparameters like the chunk size��

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