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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.

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Example

<?php
$loader = require 'vendor/autoload.php';
$loader->add('Cognesy\\Instructor\\', __DIR__.'../../src/');

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

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