Brian Sletten! Improving LLM Results with RAG

• 6pm Pizza
• 6:30-7:45 Presentation
• 7:45-8:00 Group Discussion / Q&A
• 8:00 Door prizes

As the newness and shock of the surface results of large language models (LLMs) wore off, we quickly saw the horrifying and hilarious consequences of hallucinations in the generated content space. While amusing, the idea of exposing your brand to the kind of damage that such anomalies would expose you to is going to be a major impediment to adoption.
While the large tech companies do not know yet how to stop these hallucinations from happening, new techniques have emerged to help address the problem. Retrieval augmented generation (RAG) models are gaining traction as a way of shaping the generated content based upon your understanding of a domain.
In this talk, I will provide a high level explanation of where these hallucinations come from and how RAG models will assist your attempts to roll LLMs into production more safely.

Brian Sletten is a liberal arts-educated software engineer with a focus on forward-leaning technologies. His experience has spanned many industries including retail, banking, online games, defense, finance, hospitality and health care. He has a B.S. in Computer Science from the College of William and Mary and lives in Auburn, CA. He focuses on web architecture, resource-oriented computing, social networking, the Semantic Web, data science, 3D graphics, visualization, scalable systems, security consulting and other technologies of the late 20th and early 21st Centuries. He is also a rabid reader, devoted foodie and has excellent taste in music. If pressed, he might tell you about his International Pop Recording career.