Guardrails AI: Add Safety Rails and Output Validation to Any LLM
Guardrails AI wraps LLM calls with validators for PII detection, toxicity, JSON schema, and custom rules - with automatic reask-and-retry when validation fails.
LLMs produce text that can be malformed, unsafe, or structurally incorrect. In production apps you need to handle: outputs that are not valid JSON when you expected JSON, responses that contain PII (names, emails, phone numbers), toxic language in customer-facing chatbots, and hallucinated content that violates business rules. Guardrails AI centralises this validation logic.
from guardrails import Guard
from guardrails.hub import DetectPII
guard = Guard().use(DetectPII(pii_entities=["EMAIL_ADDRESS", "PHONE_NUMBER"], on_fail="refrain"))
response = guard(
openai.chat.completions.create,
prompt="Summarise this support ticket: John called from john@acme.com about billing.",
model="gpt-4o-mini",
)
# If PII is detected in the output, response.validated_output = None (refrain)
from guardrails.validators import Validator, register_validator, PassResult, FailResult
@register_validator(name="no-code-snippets", data_type="string")
class NoCodeSnippets(Validator):
def validate(self, value: str, metadata: dict):
if "```" in value or "def " in value:
return FailResult(
error_message="Response must not contain code snippets.",
fix_value=value.split("```")[0].strip(),
)
return PassResult()
guard = Guard().use(NoCodeSnippets(on_fail="fix"))
on_fail Actions
Action
Behaviour
reask
Send validation error back to LLM and retry
refrain
Return None (silently skip bad output)
fix
Apply the fix_value from the validator
exception
Raise ValidationError
noop
Log but return the invalid output
Server Mode for Microservices
guardrails start --config config.py --port 8000
Call the validation server from any language via HTTP - useful when your LLM app is not Python.
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// written byFIG. AUTH-01
530
Mahmudul Haque Qudrati
CEO & ML Engineer
CEO and ML Engineer at Pristren. Builds AI-powered software for teams and writes about machine learning, LLMs, developer tools, and practical AI applications.
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