Scaling Marketing and Docs with a Privacy-Safe RAG Model

Every company grapples with a mountain of unstructured data, from internal documents to meeting notes to recorded calls and transcripts. Traditional analysis of this data is challenging, but the advent of Large Language Models (LLMs) promises to unlock its value. Yet LLMs, designed to learn but not to unlearn, pose privacy risks if sensitive information like PII or core IP is used during training. This session unravels the conundrum, drawing on our experience developing an LLM-based application at Skyflow that helps us quickly generate press releases, blog posts, documentation, and product one pagers. Discover how we leveraged Databricks, Streamlit, a data privacy vault, and open-source tools to create a privacy-safe RAG model for scaling our marketing, technical documentation, and product teams. You’ll gain insights into overcoming privacy challenges, ensuring responsible data use, and governing access to sensitive information while taking advantage of the promised power of LLMs.