Build an Agentic RAG Pipeline with Llama 3.1 and NVIDIA NeMo Retriever NIMs

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 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…

Source

Leave a Reply

Your email address will not be published.

Previous post Develop Production-Grade Text Retrieval Pipelines for RAG with NVIDIA NeMo Retriever 
Next post Customize Generative AI Models for Enterprise Applications with Llama 3.1