Build safe and responsible generative AI applications with guardrails

This article discusses strategies and best practices for building safe and responsible generative AI applications using guardrails to mitigate risks and vulnerabilities.

Specifically, the article covers:

• Introduction to guardrails for large language models (LLMs) and the risks associated with LLM-powered applications, such as generating toxic, biased or hallucinated content, and vulnerability to adversarial attacks
• Layers of safeguarding mechanisms and shared responsibilities between model producers and consumers for achieving safe and responsible LLM deployments
• Adding external guardrails to application architecture and implementation options like Guardrails for Amazon Bedrock, keywords/patterns/regex, Amazon Comprehend, and NVIDIA NeMo
• Evaluating the effectiveness of guardrails in terms of safety performance, LLM accuracy, latency, and robustness against evolving threats
• Conclusion emphasizing the importance of guardrails for enabling responsible AI applications while balancing progress and risk