guardrails

You can prevent prompt injection by configuring an input guard pipeline that detects and blocks malicious injection patterns before they reach your LLM. This Skill applies deterministic safety checks to constrain topic scope and validate inputs.

Scanning LLM outputs for PII requires applying output guards that detect and filter sensitive data exposure. This Skill verifies grounding against retrieved context and enforces schema formats to ensure structured, compliant responses.

The best way to moderate content and enforce compliance is applying a configurable guard pipeline to both inputs and outputs. This Skill composes cheap and expensive checks in a deterministic order to minimize latency while maximizing safety.

Yes, you can compose multiple input and output guards in a deterministic pipeline to minimize latency. This Skill allows you to order cheap and expensive checks sequentially to detect, modify, or block unsafe content efficiently.

Yes, this guardrail pipeline works without external dependencies because it operates as a standalone implementation. You can configure the input and output guards directly within your LLM service to protect user interactions.

When configuring topic constraints, consider that overly strict input guards may block legitimate user interactions. This Skill enforces topic scope deterministically, so you must carefully tune guard thresholds to balance safety with user experience.