DeanKelly751 Guardrails
Purpose
Design safety mechanisms that prevent AI systems from producing harmful or incorrect outputs.
Guardrail Layers
| Layer | What it catches | Implementation |
|---|---|---|
| Input validation | Malformed, malicious, or out-of-scope requests | Schema validation, content filtering |
| Prompt injection defense | Attempts to override instructions | Input sanitization, instruction hierarchy |
| Output validation | Incorrect format, hallucinated data, unsafe content | Schema validation, fact-checking, content filter |
| Rate limiting | Abuse, cost explosion | Token/request limits per user |
| Fallback | Complete failure | Graceful degradation to non-AI path |
Input Guardrails
- Validate input length (prevent context stuffing)
- Filter known injection patterns
- Classify intent (reject out-of-scope requests)
- Sanitize user input before injecting into prompts
Output Guardrails
- Parse output against expected schema (reject malformed)
- Check for PII leakage before returning to user
- Validate factual claims against known data sources
- Content safety filtering (toxicity, bias)
- Confidence thresholds (if model reports low confidence, fall back)
Fallback Strategy
When AI fails:
- Return cached/default response if safe
- Fall back to rule-based system
- Escalate to human
- Return honest "I don't know" with next steps
Never: Silently return potentially wrong answers as if they're correct.
Monitoring
- Track fallback rate (increasing = prompt/model degradation)
- Monitor output quality over time (drift detection)
- Log guardrail trigger reasons (identify patterns)
- Alert on unusual request patterns (potential abuse)
Related Skills
ai-workflows/knowledge-agent-design— guardrails in agent systemsai-workflows/knowledge-prompt-engineering— defensive prompt designsecurity/knowledge-secure-by-design— AI security context