How do you make AI responses safer and more reliable?

AI safety involves preventing harmful outputs and handling failures gracefully.

Input guardrails screen user prompts before sending to the model. Filter obvious prompt injection attempts, detect and block abusive content, and validate that inputs fall within expected patterns. Keep harmful content from reaching the model.

Output guardrails check responses before showing users. Use content classifiers to detect harmful content, implement keyword blocklists for critical cases, and consider having a second model review outputs for sensitive applications.

Prompt injection defenses: separate system instructions from user input clearly, use structured prompting, validate that outputs stay within expected formats. Assume adversarial users will try to manipulate your prompts.

Rate limiting prevents abuse. Limit requests per user, implement cost caps, and consider tiered access. AI calls are expensive—runaway usage can be costly.

Graceful degradation handles model failures. Have fallback behaviors when API calls fail, timeout, or return low-confidence results. Don't let AI failures break core functionality.

Human oversight: for high-stakes decisions, keep humans in the loop. Flag uncertain outputs for review. Provide ways for users to report problematic outputs.

Monitoring and logging track safety metrics: filtered content rates, user reports, unusual patterns. Regular audits of logged outputs catch emerging issues.

Document limitations clearly. Set user expectations about what the AI can and cannot do. Transparency reduces harm when AI makes mistakes.