Rail, also known as Reliable Markup Language, is much like XML crafted to make AI outputs more dependable.
For those familiar with HTML, rails structure will feel intuitive, offering a gentle learning curve
while providing a robust framework for AI.
Output specification.
The essence of rel lies in its ability to define, validate, and correct AI outputs.
With rel, developers can define expected outcomes, outline what the AI output should look like, such
as the structure and format JSON, for instance.
Specify data types.
Clearly define the type for each piece of outcome, whether it's a string, integer, list, or object.
Set quality standards establish criteria for what makes the output acceptable.
Targeting bias free text or error free code.
Implement corrective actions if the output doesn't meet the set standards.
Rail allows for predefined corrective actions such as reattempting the generation.
Applying filters or automated fixes.
The versatility and simplicity of rail makes it a stand out solution.
It's language agnostic, meaning it can be integrated across various programming languages.
Despite its simplicity, rail is capable of defining complex structures and enforcing quality standards,
ensuring that AI generated content is not just innovative, but also reliable and up to ethical standards.
Moreover, rail extends its support beyond XML to include parenting and string syntaxes, broadening
its application scope and the flexibility in handling AI outputs.