Guardrails / Validation Chain

Pain Signals — You Need This Pattern When:

• Users can inject instructions into prompts via their input (prompt injection)
• LLM outputs could contain PII, credentials, or sensitive data from the training set or context
• The LLM produces off-topic, harmful, or policy-violating content
• Downstream systems break when the LLM returns unexpected content
• There is no systematic check between the LLM's output and the user seeing it
• Compliance requires auditing what the AI produced and what was shown to users

Core Principle

Guardrails are a Chain of Responsibility applied to AI safety. Each guard inspects the input or output and can pass, modify, or block it. The guards are independent, composable, and ordered by cost (cheapest first).