Build safe generative AI applications like a Pro: Best Practices with Amazon Bedrock Guardrails

This article provides best practices for implementing Amazon Bedrock Guardrails to build safe generative AI applications while maintaining performance and user experience.

• Select foundational policies: content filtering, prompt attack prevention, sensitive information protection
• Use standard tier safeguards for better robustness, accuracy, and language support
• Test guardrails in detect mode before blocking production traffic
• Configure filter strength (LOW/MEDIUM/HIGH) based on false positive testing
• Define denied topics clearly and precisely with sample phrases
• Create custom deny topics and regex filters for specialized requirements
• Choose implementation: standalone ApplyGuardrail API or native Bedrock inference integration
• Evaluate only recent conversation turns to maintain natural flow in multi-turn conversations
• Use numerical guardrail versions in production, not DRAFT versions

The article emphasizes treating guardrails as a living system, starting with strong baselines, iteratively testing, and adjusting configurations as applications evolve.