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Overview

This implementation of Reflection Prompting with Instructor provides a structured way to encourage LLM to engage in more thorough and self-critical thinking processes, potentially leading to higher quality and more reliable outputs.

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Example

<?php

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

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



use Cognesy\Instructor\Enums\Mode;

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

use Cognesy\Instructor\Features\Validation\Contracts\CanValidateSelf;

use Cognesy\Instructor\Features\Validation\ValidationResult;

use Cognesy\Instructor\Instructor;



class ReflectiveResponse implements CanValidateSelf {

    #[Instructions('Is problem solvable and what domain expertise it requires')]

    public string $assessment;

    #[Instructions('Describe an expert persona who would be able to solve this problem, their skills and experience')]

    public string $persona;

    #[Instructions("Initial analysis and expert persona's approach to the problem")]

    public string $initialThinking;

    #[Instructions('Steps of reasoning leading to the final answer - expert persona thinking through the problem')]

    /** @var string[] */

    public array $chainOfThought;

    #[Instructions('Critical examination of the reasoning process - what could go wrong, what are the assumptions')]

    public string $reflection;

    #[Instructions('Final answer after reflection')]

    public string $finalOutput;



    // Validation method to ensure thorough reflection

    public function validate(): ValidationResult {

        $errors = [];

        if (empty($this->reflection)) {

            $errors[] = "Reflection is required for a thorough response.";

        }

        if (count($this->chainOfThought) < 2) {

            $errors[] = "Please provide at least two steps in the chain of thought.";

        }

        return ValidationResult::make($errors);

    }

}



$problem = 'Solve the equation x+y=x-y';

$solution = (new Instructor)->withConnection('anthropic')->respond(

    messages: $problem,

    responseModel: ReflectiveResponse::class,

    mode: Mode::MdJson,

    options: ['max_tokens' => 2048]

);



print("Problem:\n$problem\n\n");

dump($solution);



?>
Limiting the length of listsRestating instructions