Guardrails
Runtime checks that constrain LLM inputs and outputs to keep behavior safe and on-spec.
/ quick answer
Guardrails are programmatic policies wrapped around an LLM: input filters (PII, prompt-injection detection), output validators (schema, toxicity, factuality), and fallback behaviors. They turn a probabilistic model into a system you can ship to production with predictable failure modes. Runtime checks that constrain LLM inputs and outputs to keep behavior safe and on-spec.
/ frequently asked
What is Guardrails?
Guardrails are programmatic policies wrapped around an LLM: input filters (PII, prompt-injection detection), output validators (schema, toxicity, factuality), and fallback behaviors. They turn a probabilistic model into a system you can ship to production with predictable failure modes.
What is an example of Guardrails?
Before a support agent sends a reply, a guardrail strips customer PII from logs, validates the reply matches a JSON schema, and blocks responses that violate the refund policy.
Why does Guardrails matter?
Runtime checks that constrain LLM inputs and outputs to keep behavior safe and on-spec.
What should I learn after Guardrails?
Open the related workflows and prompts further down this page: they show how this idea is applied in a real task, step by step.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →EU AI Act Compliance
EU AI Act Compliance refers to adhering to the regulatory framework established by the European Union to govern the development, deployment, and use of artificial intelligence systems within the EU.
- →AI Governance Framework
An AI Governance Framework is a structured system of policies, processes, roles, and standards designed to guide the responsible, ethical, and compliant development and deployment of artificial intelligence systems within an organization.
- →AI Search Ranking Factors
AI search ranking factors are the criteria and signals used by generative AI models to determine the relevance, quality, and trustworthiness of information when formulating responses or generating content.
- →AI Evals
Reproducible test suites that measure LLM output quality across model, prompt and code changes.
Related workflows
Turn this into a repeatable process.
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Track agent runs, failures, cost, and review queues from one operational surface.
- →AI Operations Alert Triage
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- →Red Team an AI App
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Related tool stacks
The tools that run it in production.
- →AI Ops Observability Stack
Monitoring layer for agent runs, workflow health, cost, errors, and review queues.
- →Internal Ops Agent Stack
Tool-calling agent stack for internal triage, routing, research, and operations.
- →AI Compliance Monitoring Stack
This stack provides a set of tools and technologies for continuously monitoring AI systems to ensure ongoing adherence to regulatory requirements like the EU AI Act and data privacy laws.
- →AI Research & Knowledge Stack
Default toolset for analysts, founders and creators doing deep research with AI.
Related prompts
Reusable prompts for this job.
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