WEBVTT

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Rail, also known as Reliable Markup Language, is much like XML crafted to make AI outputs more dependable.

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For those familiar with HTML, rails structure will feel intuitive, offering a gentle learning curve

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while providing a robust framework for AI.

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Output specification.

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The essence of rel lies in its ability to define, validate, and correct AI outputs.

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With rel, developers can define expected outcomes, outline what the AI output should look like, such

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as the structure and format JSON, for instance.

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Specify data types.

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Clearly define the type for each piece of outcome, whether it's a string, integer, list, or object.

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Set quality standards establish criteria for what makes the output acceptable.

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Targeting bias free text or error free code.

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Implement corrective actions if the output doesn't meet the set standards.

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Rail allows for predefined corrective actions such as reattempting the generation.

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Applying filters or automated fixes.

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The versatility and simplicity of rail makes it a stand out solution.

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It's language agnostic, meaning it can be integrated across various programming languages.

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Despite its simplicity, rail is capable of defining complex structures and enforcing quality standards,

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ensuring that AI generated content is not just innovative, but also reliable and up to ethical standards.

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Moreover, rail extends its support beyond XML to include parenting and string syntaxes, broadening

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its application scope and the flexibility in handling AI outputs.