WEBVTT

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Here is the list of validators supported by guardrails.

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I.

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As of today, let's quickly walk through a couple of these validators to give you a better understanding

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of how they function and ensure the integrity of our outputs.

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First, we have the valid range validator.

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This is crucial when our LLM outputs numerical data.

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It checks that the numbers like user's age are within a sensible bracket, say 18 to 65 for adult services.

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If the LLM suggests someone is 120 years old, a valid range will flag this as an error based on our

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specifications.

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Next is the regex match validator.

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It's all about format compliance.

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If our LLM generates an email address, regex match ensures it fits a standard email pattern.

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This is how we maintain professional and correct communication formats in our outputs, and there is

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also a Also a validator for PII filter.

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In today's world, protecting personally identifiable information is non-negotiable.

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The PII filter scans our LMS output for sensitive data like Social Security numbers, and reacts them

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to maintain privacy and compliance with regulations.

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Validators come in various forms, each tailored to its specific needs.

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Some require specific arguments for execution.

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For instance, the valid range validator needs minimum maximum bounds.

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Others leverage metadata additional parameters to enhance their functionality.

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In instances where built in validators fall short, we have the flexibility to craft custom validators.

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Whether it's simple function or a sophisticated class inheriting from the validator base class, custom

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validators empower us to define validation processes according to our unique requirements.

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Mhm.