​
Overview

This approach to “chain of thought” improves data quality, by eliciting LLM reasoning to self-explain approach to generating the response.

With Instructor you can achieve a ‘modular’ CoT, where multiple explanations can be generated by LLM for different parts of the response, driving a more granular control and improvement of the response.

​
Example

<?php

$loader = require 'vendor/autoload.php';

$loader->add('Cognesy\\Instructor\\', __DIR__.'../../src/');



use Cognesy\Instructor\Features\Schema\Attributes\Instructions;

use Cognesy\Instructor\Instructor;



class Employee {

    #[Instructions('Think step by step to determine the correct year of employment.')]

    public string $reasoning;

    public int $yearOfEmployment;

    // ... other data fields of your employee class

}



$text = 'He was working here for 5 years. Now, in 2019, he is a manager.';



$employee = (new Instructor)->respond(

    messages: [['role' => 'user', 'content' => $text]],

    responseModel: Employee::class

);





dump($employee);



assert($employee->yearOfEmployment === 2014);

?>
Chain of SummariesSingle label classification