Guardrails AI
LLM output validation
0 case studies
2 specialists
General Dev Framework LLM output validation
Guardrails AI is used to validate and correct LLM outputs against defined rules, catching issues like hallucinations, PII leakage, off-topic responses, and format violations before they reach users. It wraps your LLM calls with validators that automatically re-prompt the model when outputs fail checks.
Install with `pip install guardrails-ai` and run `guardrails hub install hub://guardrails/regex_match` to add validators from the Guardrails Hub. Create a Guard object with your chosen validators, then call `guard()` with your LLM function and prompt. Failed validations trigger automatic retries with corrective instructions.
$ pip install guardrails-ai` and run `guardrails hub install hub://guardrails/regex_match` to add validators from the Guardrails Hub Be the first to share a Guardrails AI case study and get discovered by clients.
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JPMorgan Chase
Lead Software Engineer at JPMorgan Chase specializing in AI security, resilience, and robust autonomous systems. Builds secure AI infrastructure for one of the world's largest financial institutions.
Guardrails AI
Founder and CEO of Guardrails AI, the open-source framework for validating LLM outputs. Built the Guardrails Hub marketplace of community-contributed validators ensuring AI outputs meet safety, accuracy, and formatting requirements.
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