Guardrails are deterministic and ML-based controls that wrap LLM calls — blocking prompt injection, PII leaks, jailbreaks, off-topic queries, and unsafe completions. They turn an unpredictable model into a constrained, auditable system component.
Each stage in order — click any step to read what it does.
Defense-in-depth: input rails, model, output rails, with central policy registry.
The trade-offs worth knowing before you build this.
No single guardrail is sufficient. Stack input + model + output checks; failures should be isolated, not catastrophic.
Each rail adds 30-150ms. Run independent rails in parallel; cache decisions for repeat inputs.
Express rules as signed YAML/Rego, not hardcoded. Enables review, rollback, and audit.
Always tell the user *why* a request was blocked (briefly). Silent failures destroy trust.
Maintain a red-team set: known jailbreaks, prompt injections, PII traps. Run on every release.
Aggressive rails block legitimate users. Track allow/block ratio per cohort; tune thresholds quarterly.
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