To detect and handle outputs from Amazon Bedrock that violate your application’s content guidelines, implement a multi-layered approach combining automated filtering, post-processing, and user feedback mechanisms. Here’s how to approach this:
Detection: Automated Filtering and Moderation Use pre-built or custom moderation tools to scan model outputs before they reach end users. For example, AWS offers content moderation APIs (e.g., Amazon Comprehend’s toxicity detection) to flag harmful language, hate speech, or explicit content. You can also create custom rules (e.g., regex patterns) to block specific keywords, phrases, or sensitive data like personally identifiable information (PII). For nuanced cases, train a secondary classifier model to detect policy violations (e.g., biased statements or misinformation) specific to your application’s requirements. Always log flagged outputs for review and model improvement.
Handling: Graceful Degradation and User Communication When violations are detected, prevent the problematic content from being displayed. Replace it with a predefined safe response (e.g., “This response violates our guidelines”) or redirect the user to a human moderator. Include user-facing error messages to explain why content was blocked, balancing transparency with avoiding exposure to harmful material. For high-risk applications, implement a two-step review process where sensitive outputs are queued for human approval before being shown. Additionally, monitor repeat violations to identify abusive users or systemic model failures.
Prevention: Fine-Tuning and Guardrails Reduce the likelihood of violations by configuring Bedrock’s inference parameters (e.g., temperature, top-p sampling) to prioritize safer outputs. Use system prompts to explicitly instruct the model to avoid prohibited topics (e.g., “Do not generate medical advice”). For critical use cases, employ Bedrock’s guardrails feature to define allowed/denied topics and validate outputs against them during inference. Continuously update your detection rules and model training data based on real-world violations. For example, if users frequently encounter political bias, retrain a custom model with curated datasets to mitigate this behavior.
By combining real-time detection, clear handling protocols, and proactive prevention measures, you can maintain control over Bedrock’s outputs while preserving user trust. Regularly audit your pipeline and involve human reviewers to address edge cases automated systems might miss.