Cut Retrieval-Augmented Generation (RAG) Hallucinations by 50%

This solution brief outlines a validated Retrieval-Augmented Generation (RAG) stack designed to address those issues directly. Using NVIDIA’s AI Blueprint for RAG, it demonstrates 15× faster multimodal PDF data extraction and 50% fewer incorrect responses. These gains come from grounding models in your data with the right architecture, not from prompt tuning alone.

Inside this brief, you’ll get:

• A clear RAG reference architecture (retrieval, reranking, inference)
• How to pair RTX PRO 6000 Blackwell, H200 NVL, and HGX platforms to workload size
• The role of NIM microservices and NeMo in production pipelines
• Practical guidance on scaling from prototype to deployment without rebuilding your stack

If you’re evaluating how to make RAG reliable and deployable in your environment, this is worth your time.