Q: What’s the difference between guardrails & evaluators?
Guardrails are inline safety checks that sit directly in the request/response path. They validate inputs or outputs before anything reaches a user, so they typically are:
- Fast and deterministic – typically a few milliseconds of latency budget.
- Simple and explainable – regexes, keyword block-lists, schema or type validators, lightweight classifiers.
- Targeted at clear-cut, high-impact failures – PII leaks, profanity, disallowed instructions, SQL injection, malformed JSON, invalid code syntax, etc.
If a guardrail triggers, the system can redact, refuse, or regenerate the response. Because these checks are user-visible when they fire, false positives are treated as production bugs; teams version guardrail rules, log every trigger, and monitor rates to keep them conservative.
On the other hand, evaluators typically run after a response is produced. Evaluators measure qualities that simple rules cannot, such as factual correctness, completeness, etc. Their verdicts feed dashboards, regression tests, and model-improvement loops, but they do not block the original answer.
Evaluators are usually run asynchronously or in batch to afford heavier computation such as a LLM-as-a-Judge. Inline use of an LLM-as-Judge is possible only when the latency budget and reliability targets allow it. Slow LLM judges might be feasible in a cascade that runs on the minority of borderline cases.
Apply guardrails for immediate protection against objective failures requiring intervention. Use evaluators for monitoring and improving subjective or nuanced criteria. Together, they create layered protection.
Word of caution: Do not use llm guardrails off the shelf blindly. Always look at the prompt.
This article is part of our AI Evals FAQ, a collection of common questions (and answers) about LLM evaluation. View all FAQs or return to the homepage.