Backprompting: Leveraging Synthetic Production Data for Health Advice Guardrails

2025-08-25

AI Summary

Enterprise LLM deployments lack real-world, production-grade labeled data for health advice quality assessment prior to deployment, leading to insufficient robustness of detection models. Method: We propose BackPrompting—a novel framework that generates high-fidelity, labeled, production-like health advice texts via reverse prompting, coupled with a sparse human-feedback-driven clustering annotation strategy to construct high-quality synthetic training corpora—eliminating reliance on post-deployment real data and drastically reducing annotation cost. Contribution/Results: Integrating synthetic data into existing training sets enables training of a lightweight detector that achieves 3.73% higher accuracy than GPT-4o on health advice identification, while using only 1/400 the parameters. This approach delivers both high performance and efficiency, establishing a scalable, low-cost paradigm for early-stage development and continuous maintenance of LLM safety guardrails.

Technology Category

• Natural Language Processing: Safety and Robustness
• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

The pervasiveness of large language models (LLMs) in enterprise settings has also brought forth a significant amount of risks associated with their usage. Guardrails technologies aim to mitigate this risk by filtering LLMs' input/output text through various detectors. However, developing and maintaining robust detectors faces many challenges, one of which is the difficulty in acquiring production-quality labeled data on real LLM outputs prior to deployment. In this work, we propose backprompting, a simple yet intuitive solution to generate production-like labeled data for health advice guardrails development. Furthermore, we pair our backprompting method with a sparse human-in-the-loop clustering technique to label the generated data. Our aim is to construct a parallel corpus roughly representative of the original dataset yet resembling real LLM output. We then infuse existing datasets with our synthetic examples to produce robust training data for our detector. We test our technique in one of the most difficult and nuanced guardrails: the identification of health advice in LLM output, and demonstrate improvement versus other solutions. Our detector is able to outperform GPT-4o by up to 3.73%, despite having 400x less parameters.

Problem

• Generating synthetic production-quality data for health advice guardrails
• Addressing difficulty in acquiring labeled data from real LLM outputs
• Improving detection of health advice in LLM outputs with limited parameters

Innovation

• Backprompting generates synthetic production-like labeled data
• Sparse human-in-the-loop clustering labels generated data
• Infusing synthetic examples creates robust training datasets