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Safety
AI Guardrails Implementation
Definition
AI guardrails are protective systems that constrain LLM behavior by filtering inputs, validating outputs, and enforcing safety policies to prevent harmful or unintended AI responses.
Why It Matters
Guardrails are your safety net. Even well-aligned models can produce harmful outputs in edge cases. Guardrails provide programmatic controls that work regardless of model behavior, ensuring your application stays within acceptable bounds.
Types of Guardrails
Input Guards:
- Prompt injection detection
- PII filtering
- Topic restrictions
- Length limits
Output Guards:
- Toxicity detection
- Factual verification
- Format validation
- Sensitive content filtering
Behavioral Guards:
- Rate limiting
- Cost controls
- Action restrictions
- Human approval gates
Implementation Tools
- NeMo Guardrails: NVIDIA’s dialogue safety framework
- Guardrails AI: Open-source validation library
- Llama Guard: Meta’s safety classifier
- Azure AI Content Safety: Microsoft’s moderation API
- Custom LLM Judges: Your own safety classifiers
Best Practices
Layer guardrails (defense in depth). Log all filtered content for analysis. Balance safety with usability. Test guardrails against adversarial inputs. Update as new attack patterns emerge.