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Explore how to build enterprise RAG/LLM systems from scratch without requiring GPUs or deep neural networks in this 30-minute conference talk. Learn why traditional approaches may be unnecessary and discover alternative methods that prioritize accuracy, security, speed, and scalability while eliminating hallucinations. Examine comprehensive prompt results, model evaluation challenges, and real-time fine-tuning techniques with intuitive parameters and self-tuning capabilities. Master efficient crawling methods to retrieve and leverage contextual elements, understand hierarchical chunking and token types, and work through un-stemming processes while addressing common Python library limitations. Follow a detailed case study using Nvidia's corpus of public financial reports in PDF format to see these concepts applied in practice. Discover how to design superior user interfaces that go beyond simple prompt boxes, implement efficient backend database architectures, and handle various data sources including web content, databases, and PDFs. Understand multimodality implementation, backend agent selection through UI design, sub-LLM integration, and strategies for minimizing prompt engineering requirements in your LLM design.
