# NeMo Guardrails: A Toolkit for Controllable and Safe LLM Applications with Programmable Rails

**Authors:** Traian Rebedea, Razvan Dinu, Makesh Sreedhar, Christopher Parisien, Jonathan Cohen  
**arXiv:** [2310.10501](https://arxiv.org/abs/2310.10501)  
**Date:** 2023-10-16  
**Summary page:** https://www.summarizepaper.com/en/paper/2310.10501v1/

## Key points

- NeMo Guardrails is an open-source toolkit developed by Traian Rebedea, Razvan Dinu, Makesh Sreedhar, Christopher Parisien, and Jonathan Cohen.
- It aims to enhance the safety and controllability of large language model (LLM) applications through programmable guardrails.
- Guardrails steer conversations in a desired direction by restricting harmful topics and enforcing predefined dialogue paths and language styles.
- Unlike traditional methods, NeMo Guardrails incorporates runtime functionality inspired by dialogue management for seamless integration of user-defined rails into LLM applications.
- The flexibility and interpretability of these rails allow tailoring the behavior of LLM applications according to specific requirements and preferences.
- Research conducted by Rebedea et al. demonstrates promising initial results across various LLM providers, showcasing the effectiveness of this approach.
- By leveraging programmable rails within NeMo Guardrails, developers can create safer and more controllable LLM applications aligned with ethical standards and user expectations.

## Summary

NeMo Guardrails is an innovative open-source toolkit developed by Traian Rebedea, Razvan Dinu, Makesh Sreedhar, Christopher Parisien, and Jonathan Cohen. It aims to enhance the safety and controllability of large language model (LLM) applications by introducing programmable guardrails. These guardrails serve as guidelines for steering conversations in a desired direction by restricting harmful topics and enforcing predefined dialogue paths and language styles. Unlike traditional methods that embed guardrails during model training, NeMo Guardrails incorporates runtime functionality inspired by dialogue management. This allows developers to seamlessly integrate user-defined programmable rails into LLM applications independent of the underlying model. The flexibility and interpretability of these rails make it possible to tailor the behavior of LLM applications according to specific requirements and preferences. The research conducted by Rebedea et al., as presented in their paper "NeMo Guardrails: A Toolkit for Controllable and Safe LLM Applications with Programmable Rails," showcases promising initial results demonstrating the effectiveness of this approach across various LLM providers. By leveraging programmable rails within NeMo Guardrails, developers can create safer and more controllable LLM applications that align with ethical standards and user expectations. This work was accepted at EMNLP 2023 in the Demo track category.