Utilizing LLMs in high impact scenarios (e.g., healthcare) remains difficult due to the necessity of including private/ sensitive information in prompts. In many scenarios, AI/prompt engineers might want to include few shots examples in prompts to improve LLM performance, but the relevant examples are sensitive and need to be kept private. Any leakage of PII or PHI into LLM outputs could result in compliance problems and liability. Differential Privacy (DP) can help mitigate these issues. The Machine Learning (ML) community has recognized the importance of DP in statistical inference, but its application to generative models, like LLMs, remains limited. This talk will introduce a practical pipeline for incorporating synthetic data into prompts, offering robust privacy guarantees. This approach is also computationally efficient when compared to other approaches like privacy-focused fine-tuning or end-to-end encryption. I will demonstrate the pipeline, and I will also examine the impact of differentially private prompts on the accuracy of LLM responses.

When companies explore data quality initiatives, it’s common to wonder whether data contracts or observability is more critical. In this talk, we’ll clarify the unique roles each plays: data contracts focus on preventing known data quality issues, while data observability detects unknown issues across the entire data system. Drawing on real-world insights, we’ll show how these two approaches complement one another—think of observability as a flashlight illuminating the whole data landscape, while contracts act as a laser pointer, targeting specific areas. Attendees will learn why using both is essential for ensuring data reliability and efficiency.