08: Temperature and Sampling Parameters

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Chapter 08: Temperature and Sampling Parameters

Overview

Temperature, top_p, and top_k are the control knobs that determine how predictable or creative Claude's outputs will be. Understanding these parameters transforms your AI from a one-size-fits-all assistant into a precision tool that produces exactly the right balance of consistency and creativity for each use case.

This chapter teaches you how sampling works under the hood, when to use high vs low temperature, how to combine temperature with top_p and top_k, and how to choose the right parameters for different applications.

By the end, you'll confidently configure Claude for deterministic tasks like data extraction, creative tasks like content generation, and everything in between.

Prerequisites

Before starting, ensure you understand:

✓ Basic Claude API usage (Chapters 00-03)
✓ Prompt engineering fundamentals (Chapter 05)
✓ System prompts and role definition (Chapter 07)
✓ Basic probability concepts

Estimated Time: 45-60 minutes

What You'll Build

By the end of this chapter, you will have created:

A TemperatureGuide class that recommends optimal temperature settings for different use cases
A SamplingConfigManager for managing and validating sampling parameter configurations
A DataExtractor class using deterministic settings (temperature 0.0) for reliable data extraction
A CreativeWriter class using high temperature (1.5+) for generating unique content variations
A CodeGenerator class using focused settings (temperature 0.3) for consistent code generation
An AdaptiveAssistant that automatically adjusts temperature based on message content
A ConsistencyTester tool for measuring output consistency across different temperature settings
A SamplingABTester for systematically testing and comparing different parameter configurations
A SamplingCostAnalyzer to understand cost implications of different sampling strategies
Understanding of how stop sequences interact with sampling parameters

Objectives

By the end of this chapter, you will:

Understand how language model sampling works at the token level
Master temperature parameter control (0.0 = deterministic, 2.0 = creative)
Learn top-p (nucleus sampling) and how it filters token choices
Understand top-k sampling and when to use it
Combine temperature, top_p, and top_k for fine-grained control
Choose optimal sampling parameters for different use cases (extraction, generation, code)
Build adaptive systems that adjust parameters based on context
Measure and validate output consistency for production applications
Test sampling parameters systematically using A/B testing frameworks
Understand cost and performance implications of different sampling strategies
Apply model-specific optimal parameters for different Claude models

How Language Model Sampling Works

Token Prediction Fundamentals

php

<?php
# filename: examples/01-understanding-sampling.php
declare(strict_types=1);

/**
 * Conceptual demonstration of how Claude predicts the next token
 *
 * When Claude generates text, it:
 * 1. Looks at all tokens generated so far
 * 2. Calculates probability for EVERY possible next token
 * 3. Uses sampling parameters to choose which token to output
 * 4. Adds that token to the sequence
 * 5. Repeats until complete
 */

// Simplified example: Claude is completing "The best PHP framework is"
// Here are the top predicted tokens and their probabilities:

$tokenProbabilities = [
    'Laravel' => 0.45,      // 45% probability
    'Symfony' => 0.25,      // 25%
    'CodeIgniter' => 0.10,  // 10%
    'Yii' => 0.08,          // 8%
    'Laminas' => 0.05,      // 5%
    'Slim' => 0.04,         // 4%
    'CakePHP' => 0.03,      // 3%
    // ... thousands of other tokens with tiny probabilities
];

// Different sampling strategies will choose different tokens
// Let's simulate this:

function greedySampling(array $probs): string
{
    // Always pick the highest probability token
    arsort($probs);
    return array_key_first($probs);
}

function temperatureSampling(array $probs, float $temperature): string
{
    // Adjust probabilities based on temperature
    $adjusted = [];
    foreach ($probs as $token => $prob) {
        // Higher temperature = flatter distribution (more random)
        // Lower temperature = sharper distribution (more deterministic)
        $adjusted[$token] = pow($prob, 1 / $temperature);
    }

    // Normalize
    $sum = array_sum($adjusted);
    $normalized = array_map(fn($p) => $p / $sum, $adjusted);

    // Sample randomly based on adjusted probabilities
    return weightedRandomChoice($normalized);
}

function weightedRandomChoice(array $probabilities): string
{
    $rand = mt_rand() / mt_getrandmax();
    $cumulative = 0;

    foreach ($probabilities as $token => $probability) {
        $cumulative += $probability;
        if ($rand <= $cumulative) {
            return $token;
        }
    }

    return array_key_first($probabilities);
}

// Demonstrate different sampling strategies
echo "Original probabilities:\n";
print_r($tokenProbabilities);
echo "\n";

echo "Greedy sampling (always picks highest): ";
echo greedySampling($tokenProbabilities) . "\n\n";

echo "Temperature = 0.3 (deterministic): ";
echo temperatureSampling($tokenProbabilities, 0.3) . "\n";

echo "Temperature = 1.0 (balanced): ";
echo temperatureSampling($tokenProbabilities, 1.0) . "\n";

echo "Temperature = 2.0 (creative): ";
echo temperatureSampling($tokenProbabilities, 2.0) . "\n";

Key Insight: Temperature doesn't change what Claude knows, it changes which tokens get selected from the probability distribution.

Best Practice

Start with the default temperature (1.0) and adjust based on your needs. Lower temperature for consistency, higher for creativity. Most production applications use temperatures between 0.0 and 1.5.

Temperature Parameter

What Temperature Controls

php

<?php
# filename: examples/02-temperature-comparison.php
declare(strict_types=1);

require __DIR__ . '/../vendor/autoload.php';

use Anthropic\Anthropic;

$client = Anthropic::factory()
    ->withApiKey(getenv('ANTHROPIC_API_KEY'))
    ->make();

$prompt = 'Complete this sentence: The future of PHP is';

// Test different temperatures
$temperatures = [0.0, 0.5, 1.0, 1.5, 2.0];

foreach ($temperatures as $temp) {
    echo "Temperature {$temp}:\n";

    // Generate 3 completions to see variation
    for ($i = 1; $i <= 3; $i++) {
        $response = $client->messages()->create([
            'model' => 'claude-sonnet-4-20250514',
            'max_tokens' => 50,
            'temperature' => $temp,
            'messages' => [[
                'role' => 'user',
                'content' => $prompt
            ]]
        ]);

        echo "  {$i}. {$response->content[0]->text}\n";
    }
    echo "\n";
}

Expected pattern:

Temperature 0.0:
  1. bright, with continued evolution...
  2. bright, with continued evolution...
  3. bright, with continued evolution...
  (Nearly identical - very deterministic)

Temperature 1.0:
  1. bright, with continued evolution...
  2. promising as the language evolves...
  3. strong, particularly with modern features...
  (Some variation but still coherent)

Temperature 2.0:
  1. absolutely revolutionary with quantum...
  2. in serverless edge computing...
  3. tied to blockchain and metaverse...
  (Much more creative/unpredictable)

Temperature Scale Guide

php

<?php
# filename: examples/03-temperature-scale.php
declare(strict_types=1);

class TemperatureGuide
{
    public const DETERMINISTIC = 0.0;    // Absolute consistency
    public const FOCUSED = 0.3;          // Minimal variation
    public const BALANCED = 1.0;         // Default, natural
    public const CREATIVE = 1.5;         // More variety
    public const EXPERIMENTAL = 2.0;     // Maximum creativity

    public static function recommend(string $useCase): float
    {
        return match($useCase) {
            // Deterministic tasks
            'data_extraction',
            'json_parsing',
            'structured_output',
            'classification',
            'fact_checking' => self::DETERMINISTIC,

            // Focused but slightly flexible
            'code_review',
            'technical_documentation',
            'translation',
            'summarization' => self::FOCUSED,

            // Balanced natural language
            'general_qa',
            'explanation',
            'tutoring',
            'conversational' => self::BALANCED,

            // Creative tasks
            'brainstorming',
            'content_generation',
            'story_writing',
            'marketing_copy' => self::CREATIVE,

            // Highly experimental
            'ideation',
            'creative_writing',
            'unusual_perspectives' => self::EXPERIMENTAL,

            default => self::BALANCED,
        };
    }

    public static function describe(float $temperature): string
    {
        return match(true) {
            $temperature < 0.3 => 'Highly deterministic - minimal variation',
            $temperature < 0.7 => 'Mostly deterministic - slight variation',
            $temperature < 1.2 => 'Balanced - natural variation',
            $temperature < 1.7 => 'Creative - increased variation',
            default => 'Highly creative - maximum variation',
        };
    }
}

// Usage examples
$useCases = [
    'Extract contact info from business card',
    'Write creative marketing tagline',
    'Explain how Laravel routing works',
    'Generate unique product descriptions',
    'Parse JSON from unstructured text',
];

foreach ($useCases as $useCase) {
    $category = match(true) {
        str_contains($useCase, 'Extract') => 'data_extraction',
        str_contains($useCase, 'creative') => 'content_generation',
        str_contains($useCase, 'Explain') => 'explanation',
        str_contains($useCase, 'Generate') => 'brainstorming',
        str_contains($useCase, 'Parse') => 'structured_output',
        default => 'general_qa'
    };

    $temp = TemperatureGuide::recommend($category);
    $desc = TemperatureGuide::describe($temp);

    echo "Use case: {$useCase}\n";
    echo "Recommended temperature: {$temp} ({$desc})\n\n";
}

Top-P (Nucleus Sampling)

Understanding Top-P

Top-p (also called nucleus sampling) considers only the most probable tokens whose cumulative probability reaches the threshold p.

INFO

The default top_p value is typically 0.9, which works well for most use cases. Lower values (0.5-0.8) create more focused outputs, while higher values (0.95-1.0) allow more diversity.

php

<?php
# filename: examples/04-top-p-visualization.php
declare(strict_types=1);

/**
 * Demonstrates how top_p filters token choices
 */

// Token probabilities from Claude's prediction
$tokenProbabilities = [
    'Laravel' => 0.35,
    'Symfony' => 0.25,
    'CodeIgniter' => 0.15,
    'Yii' => 0.10,
    'Laminas' => 0.05,
    'Slim' => 0.04,
    'CakePHP' => 0.03,
    'Phalcon' => 0.02,
    'FuelPHP' => 0.01,
];

function applyTopP(array $probs, float $topP): array
{
    // Sort by probability descending
    arsort($probs);

    $cumulative = 0;
    $filtered = [];

    foreach ($probs as $token => $prob) {
        $cumulative += $prob;
        $filtered[$token] = $prob;

        if ($cumulative >= $topP) {
            break;
        }
    }

    return $filtered;
}

// Compare different top_p values
$topPValues = [0.5, 0.8, 0.9, 1.0];

foreach ($topPValues as $topP) {
    echo "top_p = {$topP}:\n";

    $filtered = applyTopP($tokenProbabilities, $topP);

    echo "  Tokens considered: " . count($filtered) . "\n";
    echo "  Options: " . implode(', ', array_keys($filtered)) . "\n";
    echo "  Cumulative probability: " . round(array_sum($filtered) * 100, 1) . "%\n\n";
}

Output:

top_p = 0.5:
  Tokens considered: 2
  Options: Laravel, Symfony
  Cumulative probability: 60%

top_p = 0.8:
  Tokens considered: 4
  Options: Laravel, Symfony, CodeIgniter, Yii
  Cumulative probability: 85%

top_p = 0.9:
  Tokens considered: 5
  Options: Laravel, Symfony, CodeIgniter, Yii, Laminas
  Cumulative probability: 90%

top_p = 1.0:
  Tokens considered: 9
  Options: (all)
  Cumulative probability: 100%

Top-P in Practice

php

<?php
# filename: examples/05-top-p-comparison.php
declare(strict_types=1);

require __DIR__ . '/../vendor/autoload.php';

use Anthropic\Anthropic;
use Anthropic\Contracts\ClientContract;

$client = Anthropic::factory()
    ->withApiKey(getenv('ANTHROPIC_API_KEY'))
    ->make();

function testTopP(ClientContract $client, float $topP): array
{
    $responses = [];

    for ($i = 0; $i < 3; $i++) {
        $response = $client->messages()->create([
            'model' => 'claude-sonnet-4-20250514',
            'max_tokens' => 100,
            'temperature' => 1.0,  // Keep temperature constant
            'top_p' => $topP,
            'messages' => [[
                'role' => 'user',
                'content' => 'Name 3 unique PHP frameworks:'
            ]]
        ]);

        $responses[] = $response->content[0]->text;
    }

    return $responses;
}

// Compare different top_p values
echo "top_p = 0.5 (conservative):\n";
$responses = testTopP($client, 0.5);
foreach ($responses as $i => $resp) {
    echo "  " . ($i + 1) . ". " . substr($resp, 0, 100) . "...\n";
}
echo "\n";

echo "top_p = 0.9 (standard):\n";
$responses = testTopP($client, 0.9);
foreach ($responses as $i => $resp) {
    echo "  " . ($i + 1) . ". " . substr($resp, 0, 100) . "...\n";
}
echo "\n";

echo "top_p = 1.0 (all options):\n";
$responses = testTopP($client, 1.0);
foreach ($responses as $i => $resp) {
    echo "  " . ($i + 1) . ". " . substr($resp, 0, 100) . "...\n";
}

Top-K Sampling

Understanding Top-K

Top-k limits consideration to only the k most probable tokens.

php

<?php
# filename: examples/06-top-k-demonstration.php
declare(strict_types=1);

/**
 * Top-k is simpler than top-p: just take the top k tokens
 */

function applyTopK(array $probs, int $topK): array
{
    // Sort by probability descending
    arsort($probs);

    // Take only top k tokens
    return array_slice($probs, 0, $topK, true);
}

$tokenProbabilities = [
    'Laravel' => 0.35,
    'Symfony' => 0.25,
    'CodeIgniter' => 0.15,
    'Yii' => 0.10,
    'Laminas' => 0.05,
    'Slim' => 0.04,
    'CakePHP' => 0.03,
    'Phalcon' => 0.02,
    'FuelPHP' => 0.01,
];

$topKValues = [1, 3, 5, 10];

foreach ($topKValues as $k) {
    echo "top_k = {$k}:\n";

    $filtered = applyTopK($tokenProbabilities, $k);

    echo "  Options: " . implode(', ', array_keys($filtered)) . "\n";
    echo "  Probability coverage: " . round(array_sum($filtered) * 100, 1) . "%\n\n";
}

Output:

top_k = 1:
  Options: Laravel
  Probability coverage: 35%
  (Most deterministic - only best option)

top_k = 3:
  Options: Laravel, Symfony, CodeIgniter
  Probability coverage: 75%

top_k = 5:
  Options: Laravel, Symfony, CodeIgniter, Yii, Laminas
  Probability coverage: 90%

top_k = 10:
  Options: (all 9 tokens)
  Probability coverage: 100%

Top-K Usage

php

<?php
# filename: examples/07-top-k-usage.php
declare(strict_types=1);

require __DIR__ . '/../vendor/autoload.php';

use Anthropic\Anthropic;

// Note: Claude API doesn't expose top_k as directly as some models
// But understanding it helps grasp sampling mechanics

$client = Anthropic::factory()
    ->withApiKey(getenv('ANTHROPIC_API_KEY'))
    ->make();

// Conceptual usage (if supported):
$response = $client->messages()->create([
    'model' => 'claude-sonnet-4-20250514',
    'max_tokens' => 200,
    'temperature' => 1.0,
    'top_k' => 40,  // Consider only top 40 tokens at each step
    'messages' => [[
        'role' => 'user',
        'content' => 'Generate a creative function name for user authentication'
    ]]
]);

// Lower top_k = more focused/conventional
// Higher top_k = more creative/unusual

Combining Sampling Parameters

Temperature + Top-P Interaction

php

<?php
# filename: examples/08-parameter-combinations.php
declare(strict_types=1);

require __DIR__ . '/../vendor/autoload.php';

use Anthropic\Anthropic;

$client = Anthropic::factory()
    ->withApiKey(getenv('ANTHROPIC_API_KEY'))
    ->make();

class SamplingStrategy
{
    public function __construct(
        public readonly float $temperature,
        public readonly float $topP,
        public readonly string $description,
        public readonly array $bestFor
    ) {}
}

class SamplingPresets
{
    public const STRATEGIES = [
        'deterministic' => [
            'temperature' => 0.0,
            'top_p' => 1.0,
            'description' => 'Maximum consistency, minimal variation',
            'best_for' => ['data extraction', 'JSON parsing', 'classification']
        ],

        'focused' => [
            'temperature' => 0.3,
            'top_p' => 0.8,
            'description' => 'Consistent but not robotic',
            'best_for' => ['code review', 'documentation', 'translation']
        ],

        'balanced' => [
            'temperature' => 1.0,
            'top_p' => 0.9,
            'description' => 'Natural variation, good for most tasks',
            'best_for' => ['conversation', 'explanation', 'Q&A']
        ],

        'creative' => [
            'temperature' => 1.3,
            'top_p' => 0.95,
            'description' => 'Increased creativity while staying coherent',
            'best_for' => ['brainstorming', 'content generation', 'writing']
        ],

        'experimental' => [
            'temperature' => 1.8,
            'top_p' => 1.0,
            'description' => 'Maximum creativity and exploration',
            'best_for' => ['ideation', 'creative writing', 'novel solutions']
        ],
    ];

    public static function get(string $name): SamplingStrategy
    {
        $config = self::STRATEGIES[$name] ?? self::STRATEGIES['balanced'];

        return new SamplingStrategy(
            temperature: $config['temperature'],
            topP: $config['top_p'],
            description: $config['description'],
            bestFor: $config['best_for']
        );
    }

    public static function forTask(string $task): SamplingStrategy
    {
        $task = strtolower($task);

        foreach (self::STRATEGIES as $name => $config) {
            foreach ($config['best_for'] as $useCase) {
                if (str_contains($task, $useCase)) {
                    return self::get($name);
                }
            }
        }

        return self::get('balanced');
    }
}

// Usage
$strategy = SamplingPresets::get('creative');

$response = $client->messages()->create([
    'model' => 'claude-sonnet-4-20250514',
    'max_tokens' => 500,
    'temperature' => $strategy->temperature,
    'top_p' => $strategy->topP,
    'messages' => [[
        'role' => 'user',
        'content' => 'Generate unique variable names for a payment processing system'
    ]]
]);

echo "Using strategy: {$strategy->description}\n";
echo "Temperature: {$strategy->temperature}, Top-p: {$strategy->topP}\n\n";
echo $response->content[0]->text;

Configuration Manager

php

<?php
# filename: src/SamplingConfigManager.php
declare(strict_types=1);

namespace CodeWithPHP\Claude;

class SamplingConfigManager
{
    private array $configs = [];
    private string $currentConfig = 'default';

    public function __construct()
    {
        // Load default configurations
        $this->registerConfig('default', [
            'temperature' => 1.0,
            'top_p' => 0.9,
        ]);

        $this->registerConfig('deterministic', [
            'temperature' => 0.0,
            'top_p' => 1.0,
        ]);

        $this->registerConfig('creative', [
            'temperature' => 1.5,
            'top_p' => 0.95,
        ]);
    }

    public function registerConfig(string $name, array $config): void
    {
        $this->validateConfig($config);
        $this->configs[$name] = $config;
    }

    public function useConfig(string $name): void
    {
        if (!isset($this->configs[$name])) {
            throw new \InvalidArgumentException("Config '{$name}' not found");
        }
        $this->currentConfig = $name;
    }

    public function getConfig(?string $name = null): array
    {
        $name = $name ?? $this->currentConfig;
        return $this->configs[$name] ?? $this->configs['default'];
    }

    public function mergeConfig(array $overrides): array
    {
        return array_merge($this->getConfig(), $overrides);
    }

    private function validateConfig(array $config): void
    {
        if (isset($config['temperature'])) {
            $temp = $config['temperature'];
            if ($temp < 0 || $temp > 2) {
                throw new \InvalidArgumentException('Temperature must be between 0 and 2');
            }
        }

        if (isset($config['top_p'])) {
            $topP = $config['top_p'];
            if ($topP < 0 || $topP > 1) {
                throw new \InvalidArgumentException('top_p must be between 0 and 1');
            }
        }
    }

    public function listConfigs(): array
    {
        return array_keys($this->configs);
    }
}

// Usage
require __DIR__ . '/../vendor/autoload.php';

use Anthropic\Anthropic;

$client = Anthropic::factory()
    ->withApiKey(getenv('ANTHROPIC_API_KEY'))
    ->make();

$manager = new SamplingConfigManager();

// Register custom config
$manager->registerConfig('code_generation', [
    'temperature' => 0.4,
    'top_p' => 0.85,
]);

// Use a config
$manager->useConfig('code_generation');
$config = $manager->getConfig();

$response = $client->messages()->create([
    'model' => 'claude-sonnet-4-20250514',
    'max_tokens' => 1024,
    ...$config,  // Spread operator to merge config
    'messages' => [[
        'role' => 'user',
        'content' => 'Generate a PHP class for user authentication'
    ]]
]);

Use Case Specific Configurations

Data Extraction (Deterministic)

php

<?php
# filename: examples/09-deterministic-extraction.php
declare(strict_types=1);

require __DIR__ . '/../vendor/autoload.php';

use Anthropic\Contracts\ClientContract;

class DataExtractor
{
    public function __construct(
        private ClientContract $client
    ) {}

    public function extractJSON(string $text, array $schema): array
    {
        $schemaDescription = json_encode($schema, JSON_PRETTY_PRINT);

        $response = $this->client->messages()->create([
            'model' => 'claude-sonnet-4-20250514',
            'max_tokens' => 2048,
            'temperature' => 0.0,  // Maximum determinism
            'top_p' => 1.0,
            'system' => 'Extract structured data as valid JSON. Return ONLY the JSON object, no other text.',
            'messages' => [[
                'role' => 'user',
                'content' => "Extract data matching this schema:\n\n{$schemaDescription}\n\nFrom this text:\n\n{$text}"
            ]]
        ]);

        $json = $response->content[0]->text;

        // Clean potential markdown wrapping
        if (preg_match('/```json\s*(.*?)\s*```/s', $json, $matches)) {
            $json = $matches[1];
        }

        return json_decode($json, true) ?? [];
    }
}

// Usage
require __DIR__ . '/../vendor/autoload.php';

use Anthropic\Anthropic;

$client = Anthropic::factory()
    ->withApiKey(getenv('ANTHROPIC_API_KEY'))
    ->make();

$extractor = new DataExtractor($client);

$businessCard = <<<TEXT
John Smith
Senior Software Engineer
Acme Corporation
Email: john.smith@acme.com
Phone: +1-555-123-4567
Website: https://acme.com
TEXT;

$schema = [
    'name' => 'string',
    'title' => 'string',
    'company' => 'string',
    'email' => 'string',
    'phone' => 'string',
    'website' => 'string',
];

$data = $extractor->extractJSON($businessCard, $schema);
print_r($data);

// Run multiple times - should get IDENTICAL results
// This is critical for reliable data processing

Content Generation (Creative)

php

<?php
# filename: examples/10-creative-generation.php
declare(strict_types=1);

require __DIR__ . '/../vendor/autoload.php';

use Anthropic\Contracts\ClientContract;

class CreativeWriter
{
    public function __construct(
        private ClientContract $client
    ) {}

    public function generateVariations(
        string $prompt,
        int $count = 5,
        float $temperature = 1.5
    ): array {
        $variations = [];

        for ($i = 0; $i < $count; $i++) {
            $response = $this->client->messages()->create([
                'model' => 'claude-sonnet-4-20250514',
                'max_tokens' => 500,
                'temperature' => $temperature,  // High for creativity
                'top_p' => 0.95,               // Broad token consideration
                'messages' => [[
                    'role' => 'user',
                    'content' => $prompt
                ]]
            ]);

            $variations[] = $response->content[0]->text;
        }

        return $variations;
    }

    public function generateUnique(string $prompt, array $previous = []): string
    {
        $attempt = 0;
        $maxAttempts = 10;

        while ($attempt < $maxAttempts) {
            $response = $this->client->messages()->create([
                'model' => 'claude-sonnet-4-20250514',
                'max_tokens' => 500,
                'temperature' => 1.6,  // Higher for uniqueness
                'top_p' => 0.98,
                'messages' => [[
                    'role' => 'user',
                    'content' => $prompt . "\n\nGenerate something completely unique and different from these:\n" . implode("\n", $previous)
                ]]
            ]);

            $result = $response->content[0]->text;

            // Check similarity against previous
            $isSimilar = false;
            foreach ($previous as $prev) {
                similar_text($prev, $result, $similarity);
                if ($similarity > 70) {
                    $isSimilar = true;
                    break;
                }
            }

            if (!$isSimilar) {
                return $result;
            }

            $attempt++;
        }

        throw new \RuntimeException('Could not generate unique content');
    }
}

// Usage
require __DIR__ . '/../vendor/autoload.php';

use Anthropic\Anthropic;

$client = Anthropic::factory()
    ->withApiKey(getenv('ANTHROPIC_API_KEY'))
    ->make();

$writer = new CreativeWriter($client);

echo "Generating 5 creative taglines:\n\n";

$taglines = $writer->generateVariations(
    prompt: 'Create a catchy tagline for a modern PHP framework focused on developer experience',
    count: 5,
    temperature: 1.5
);

foreach ($taglines as $i => $tagline) {
    echo ($i + 1) . ". {$tagline}\n\n";
}

Code Generation (Focused)

php

<?php
# filename: examples/11-focused-code-generation.php
declare(strict_types=1);

require __DIR__ . '/../vendor/autoload.php';

use Anthropic\Contracts\ClientContract;

class CodeGenerator
{
    public function __construct(
        private ClientContract $client
    ) {}

    public function generateFunction(
        string $description,
        array $parameters = [],
        ?string $returnType = null
    ): string {
        $paramsList = '';
        foreach ($parameters as $name => $type) {
            $paramsList .= "{$type} \${$name}, ";
        }
        $paramsList = rtrim($paramsList, ', ');

        $returnHint = $returnType ? ": {$returnType}" : '';

        $response = $this->client->messages()->create([
            'model' => 'claude-sonnet-4-20250514',
            'max_tokens' => 1024,
            'temperature' => 0.3,  // Low for consistent, reliable code
            'top_p' => 0.85,       // Focused on conventional patterns
            'system' => 'Generate clean PHP 8.4+ code following PSR-12 standards. Include type hints, return types, and PHPDoc comments.',
            'messages' => [[
                'role' => 'user',
                'content' => "Generate a PHP function:\n\nDescription: {$description}\nParameters: {$paramsList}\nReturn type: {$returnHint}\n\nUse declare(strict_types=1) and modern PHP features."
            ]]
        ]);

        return $response->content[0]->text;
    }

    public function refactorCode(string $code, array $improvements = []): string
    {
        $improvementsList = implode("\n", array_map(fn($i) => "- {$i}", $improvements));

        $response = $this->client->messages()->create([
            'model' => 'claude-sonnet-4-20250514',
            'max_tokens' => 2048,
            'temperature' => 0.2,  // Very focused - we want reliable refactoring
            'top_p' => 0.8,
            'system' => 'Refactor PHP code following best practices. Maintain functionality while improving code quality.',
            'messages' => [[
                'role' => 'user',
                'content' => "Refactor this code:\n\n```php\n{$code}\n```\n\nFocus on:\n{$improvementsList}"
            ]]
        ]);

        return $response->content[0]->text;
    }
}

// Usage
require __DIR__ . '/../vendor/autoload.php';

use Anthropic\Anthropic;

$client = Anthropic::factory()
    ->withApiKey(getenv('ANTHROPIC_API_KEY'))
    ->make();

$generator = new CodeGenerator($client);

$function = $generator->generateFunction(
    description: 'Validate and sanitize email addresses',
    parameters: ['email' => 'string'],
    returnType: 'string|null'
);

echo $function;

Adaptive Temperature Based on Context

Dynamic Temperature Adjustment

php

<?php
# filename: examples/12-adaptive-temperature.php
declare(strict_types=1);

require __DIR__ . '/../vendor/autoload.php';

use Anthropic\Contracts\ClientContract;

class AdaptiveAssistant
{
    public function __construct(
        private ClientContract $client
    ) {}

    public function respond(string $message): string
    {
        $temperature = $this->determineTemperature($message);
        $topP = $this->determineTopP($message);

        $response = $this->client->messages()->create([
            'model' => 'claude-sonnet-4-20250514',
            'max_tokens' => 2048,
            'temperature' => $temperature,
            'top_p' => $topP,
            'messages' => [[
                'role' => 'user',
                'content' => $message
            ]]
        ]);

        return $response->content[0]->text;
    }

    private function determineTemperature(string $message): float
    {
        $message = strtolower($message);

        // Deterministic tasks
        if ($this->contains($message, ['extract', 'parse', 'classify', 'categorize', 'validate'])) {
            return 0.0;
        }

        // Focused tasks
        if ($this->contains($message, ['review', 'analyze', 'explain', 'document', 'translate'])) {
            return 0.3;
        }

        // Creative tasks
        if ($this->contains($message, ['generate', 'create', 'write', 'brainstorm', 'suggest'])) {
            return 1.5;
        }

        // Code-related (focused)
        if ($this->contains($message, ['code', 'function', 'class', 'refactor', 'implement'])) {
            return 0.4;
        }

        // Default: balanced
        return 1.0;
    }

    private function determineTopP(string $message): float
    {
        $message = strtolower($message);

        // Deterministic: consider all options
        if ($this->contains($message, ['extract', 'parse'])) {
            return 1.0;
        }

        // Creative: broad consideration
        if ($this->contains($message, ['generate', 'brainstorm', 'unique'])) {
            return 0.95;
        }

        // Standard
        return 0.9;
    }

    private function contains(string $haystack, array $needles): bool
    {
        foreach ($needles as $needle) {
            if (str_contains($haystack, $needle)) {
                return true;
            }
        }
        return false;
    }
}

// Usage
require __DIR__ . '/../vendor/autoload.php';

use Anthropic\Anthropic;

$client = Anthropic::factory()
    ->withApiKey(getenv('ANTHROPIC_API_KEY'))
    ->make();

$assistant = new AdaptiveAssistant($client);

// This will use low temperature (deterministic)
echo $assistant->respond('Extract email addresses from this text: ...') . "\n\n";

// This will use high temperature (creative)
echo $assistant->respond('Generate creative marketing taglines for...') . "\n\n";

// This will use medium-low temperature (focused)
echo $assistant->respond('Review this PHP code for issues...') . "\n\n";

Stop Sequences and Sampling Interaction

How Stop Sequences Affect Sampling

Stop sequences interact with sampling parameters in important ways:

php

<?php
# filename: examples/14-stop-sequences-sampling.php
declare(strict_types=1);

require __DIR__ . '/../vendor/autoload.php';

use Anthropic\Anthropic;

$client = Anthropic::factory()
    ->withApiKey(getenv('ANTHROPIC_API_KEY'))
    ->make();

// Stop sequences can help control output length with high temperature
// High temperature might generate longer outputs, but stop sequences provide a safety net

$response = $client->messages()->create([
    'model' => 'claude-sonnet-4-20250514',
    'max_tokens' => 1000,
    'temperature' => 1.5,  // High creativity
    'top_p' => 0.95,
    'stop_sequences' => ['</response>', '---END---'],  // Stop when these appear
    'messages' => [[
        'role' => 'user',
        'content' => 'Generate a creative product description. End with </response>'
    ]]
]);

// Stop sequences work independently of sampling parameters
// They provide deterministic stopping points regardless of temperature

Best Practice

Use stop sequences with high temperature to prevent runaway generation. Stop sequences provide deterministic control over output length, complementing the creative randomness of high temperature.

A/B Testing Sampling Parameters

Systematic Parameter Testing

For production applications, systematically test different sampling parameters to find optimal settings:

php

<?php
# filename: examples/15-ab-testing-sampling.php
declare(strict_types=1);

require __DIR__ . '/../vendor/autoload.php';

use Anthropic\Anthropic;
use Anthropic\Contracts\ClientContract;

class SamplingABTester
{
    public function __construct(
        private ClientContract $client
    ) {}

    public function testConfigurations(
        string $prompt,
        array $configurations,
        int $samplesPerConfig = 5
    ): array {
        $results = [];

        foreach ($configurations as $name => $config) {
            $responses = [];
            $totalTokens = 0;
            $totalTime = 0;

            for ($i = 0; $i < $samplesPerConfig; $i++) {
                $start = microtime(true);

                $response = $this->client->messages()->create([
                    'model' => 'claude-sonnet-4-20250514',
                    'max_tokens' => 500,
                    'temperature' => $config['temperature'] ?? 1.0,
                    'top_p' => $config['top_p'] ?? 0.9,
                    'messages' => [[
                        'role' => 'user',
                        'content' => $prompt
                    ]]
                ]);

                $totalTime += microtime(true) - $start;
                $totalTokens += $response->usage->outputTokens ?? 0;
                $responses[] = $response->content[0]->text;
            }

            $results[$name] = [
                'config' => $config,
                'responses' => $responses,
                'avg_length' => array_sum(array_map('strlen', $responses)) / count($responses),
                'avg_tokens' => $totalTokens / $samplesPerConfig,
                'avg_time' => $totalTime / $samplesPerConfig,
                'consistency' => $this->calculateConsistency($responses),
                'uniqueness' => count(array_unique($responses)) / count($responses),
            ];
        }

        return $results;
    }

    private function calculateConsistency(array $responses): float
    {
        if (count($responses) < 2) {
            return 1.0;
        }

        $similarities = [];
        $count = count($responses);

        for ($i = 0; $i < $count; $i++) {
            for ($j = $i + 1; $j < $count; $j++) {
                similar_text($responses[$i], $responses[$j], $percent);
                $similarities[] = $percent;
            }
        }

        return array_sum($similarities) / count($similarities);
    }

    public function recommendBestConfig(array $results, string $priority = 'balanced'): string
    {
        $scored = [];

        foreach ($results as $name => $result) {
            $score = match($priority) {
                'consistency' => $result['consistency'],
                'creativity' => 1 - $result['consistency'],
                'cost' => 1 / ($result['avg_tokens'] + 1),
                'balanced' => ($result['consistency'] + (1 - $result['consistency']) + (1 / ($result['avg_tokens'] + 1))) / 3,
                default => $result['consistency'],
            };

            $scored[$name] = $score;
        }

        arsort($scored);
        return array_key_first($scored);
    }
}

// Usage
$tester = new SamplingABTester($client);

$configurations = [
    'deterministic' => ['temperature' => 0.0, 'top_p' => 1.0],
    'focused' => ['temperature' => 0.3, 'top_p' => 0.8],
    'balanced' => ['temperature' => 1.0, 'top_p' => 0.9],
    'creative' => ['temperature' => 1.5, 'top_p' => 0.95],
];

$results = $tester->testConfigurations(
    prompt: 'Generate a product tagline for a PHP framework',
    configurations: $configurations,
    samplesPerConfig: 5
);

// Find best config for your priority
$best = $tester->recommendBestConfig($results, priority: 'balanced');

echo "Recommended configuration: {$best}\n";
print_r($results[$best]);

Model-Specific Considerations

Optimal Parameters by Model

Different Claude models may have slightly different optimal temperature ranges:

php

<?php
# filename: examples/16-model-specific-parameters.php
declare(strict_types=1);

class ModelSpecificSampling
{
    public const MODEL_GUIDELINES = [
        'claude-3-haiku-20240307' => [
            'deterministic' => ['temperature' => 0.0, 'top_p' => 1.0],
            'balanced' => ['temperature' => 1.0, 'top_p' => 0.9],
            'creative' => ['temperature' => 1.3, 'top_p' => 0.95],
            'note' => 'Haiku is faster and cheaper - good for high-volume tasks',
        ],
        'claude-3-sonnet-20240229' => [
            'deterministic' => ['temperature' => 0.0, 'top_p' => 1.0],
            'balanced' => ['temperature' => 1.0, 'top_p' => 0.9],
            'creative' => ['temperature' => 1.5, 'top_p' => 0.95],
            'note' => 'Sonnet balances quality and cost - default for most tasks',
        ],
        'claude-sonnet-4-20250514' => [
            'deterministic' => ['temperature' => 0.0, 'top_p' => 1.0],
            'balanced' => ['temperature' => 1.0, 'top_p' => 0.9],
            'creative' => ['temperature' => 1.5, 'top_p' => 0.95],
            'note' => 'Latest Sonnet model - best quality, use for complex tasks',
        ],
        'claude-3-opus-20240229' => [
            'deterministic' => ['temperature' => 0.0, 'top_p' => 1.0],
            'balanced' => ['temperature' => 1.0, 'top_p' => 0.9],
            'creative' => ['temperature' => 1.5, 'top_p' => 0.95],
            'note' => 'Most capable model - use for complex reasoning tasks',
        ],
    ];

    public static function getRecommendedConfig(string $model, string $useCase): array
    {
        $guidelines = self::MODEL_GUIDELINES[$model] ?? self::MODEL_GUIDELINES['claude-sonnet-4-20250514'];
        
        return match($useCase) {
            'extraction', 'parsing', 'classification' => $guidelines['deterministic'],
            'code', 'review', 'documentation' => ['temperature' => 0.3, 'top_p' => 0.8],
            'conversation', 'qa', 'explanation' => $guidelines['balanced'],
            'generation', 'brainstorming', 'creative' => $guidelines['creative'],
            default => $guidelines['balanced'],
        };
    }
}

// Usage
$config = ModelSpecificSampling::getRecommendedConfig(
    model: 'claude-sonnet-4-20250514',
    useCase: 'extraction'
);

echo "Recommended config: ";
print_r($config);

Model Differences

While sampling parameters work similarly across Claude models, newer models (like Sonnet 4) may handle high temperatures more gracefully. Always test with your specific model and use case.

Performance and Cost Implications

Sampling Parameters and Token Usage

Higher temperature can lead to longer outputs, which increases token usage and costs:

php

<?php
# filename: examples/17-cost-implications.php
declare(strict_types=1);

require __DIR__ . '/../vendor/autoload.php';

use Anthropic\Anthropic;
use Anthropic\Contracts\ClientContract;

class SamplingCostAnalyzer
{
    public function __construct(
        private ClientContract $client
    ) {}

    public function analyzeCostImpact(
        string $prompt,
        array $temperatures = [0.0, 0.5, 1.0, 1.5, 2.0],
        int $samples = 5
    ): array {
        $results = [];

        foreach ($temperatures as $temp) {
            $tokens = [];
            $lengths = [];

            for ($i = 0; $i < $samples; $i++) {
                $response = $this->client->messages()->create([
                    'model' => 'claude-sonnet-4-20250514',
                    'max_tokens' => 1000,
                    'temperature' => $temp,
                    'messages' => [[
                        'role' => 'user',
                        'content' => $prompt
                    ]]
                ]);

                $tokens[] = $response->usage->outputTokens ?? 0;
                $lengths[] = strlen($response->content[0]->text);
            }

            $results[] = [
                'temperature' => $temp,
                'avg_tokens' => array_sum($tokens) / count($tokens),
                'avg_length' => array_sum($lengths) / count($lengths),
                'cost_per_1k_requests' => (array_sum($tokens) / count($tokens)) * 1000 * 0.003, // Example pricing
            ];
        }

        return $results;
    }
}

// Usage
$analyzer = new SamplingCostAnalyzer($client);

$results = $analyzer->analyzeCostImpact(
    prompt: 'Write a detailed product description',
    temperatures: [0.0, 1.0, 1.5, 2.0]
);

foreach ($results as $result) {
    echo "Temperature {$result['temperature']}: ";
    echo "Avg {$result['avg_tokens']} tokens, ";
    echo "Est. cost per 1K requests: \${$result['cost_per_1k_requests']}\n";
}

Cost Consideration

Higher temperature often produces longer, more varied outputs, which increases token usage and costs. For high-volume applications, test whether lower temperature with better prompts achieves similar results at lower cost.

Measuring Output Consistency

Consistency Tester

php

<?php
# filename: examples/13-consistency-tester.php
declare(strict_types=1);

require __DIR__ . '/../vendor/autoload.php';

use Anthropic\Contracts\ClientContract;

class ConsistencyTester
{
    public function __construct(
        private ClientContract $client
    ) {}

    public function measureConsistency(
        string $prompt,
        float $temperature,
        int $samples = 10
    ): array {
        $responses = [];

        for ($i = 0; $i < $samples; $i++) {
            $response = $this->client->messages()->create([
                'model' => 'claude-sonnet-4-20250514',
                'max_tokens' => 500,
                'temperature' => $temperature,
                'messages' => [[
                    'role' => 'user',
                    'content' => $prompt
                ]]
            ]);

            $responses[] = $response->content[0]->text;
        }

        return [
            'responses' => $responses,
            'unique_count' => count(array_unique($responses)),
            'consistency_score' => $this->calculateConsistency($responses),
            'average_length' => array_sum(array_map('strlen', $responses)) / count($responses),
        ];
    }

    private function calculateConsistency(array $responses): float
    {
        if (count($responses) < 2) {
            return 1.0;
        }

        $similarities = [];
        $count = count($responses);

        for ($i = 0; $i < $count; $i++) {
            for ($j = $i + 1; $j < $count; $j++) {
                similar_text($responses[$i], $responses[$j], $percent);
                $similarities[] = $percent;
            }
        }

        return array_sum($similarities) / count($similarities);
    }

    public function findOptimalTemperature(
        string $prompt,
        float $targetConsistency = 80.0,
        float $step = 0.1
    ): float {
        $temperature = 0.0;

        while ($temperature <= 2.0) {
            $result = $this->measureConsistency($prompt, $temperature, 5);

            if ($result['consistency_score'] >= $targetConsistency) {
                return $temperature;
            }

            $temperature += $step;
        }

        return 0.0; // Fallback to deterministic
    }
}

// Usage
require __DIR__ . '/../vendor/autoload.php';

use Anthropic\Anthropic;

$client = Anthropic::factory()
    ->withApiKey(getenv('ANTHROPIC_API_KEY'))
    ->make();

$tester = new ConsistencyTester($client);

echo "Testing consistency at different temperatures:\n\n";

foreach ([0.0, 0.5, 1.0, 1.5] as $temp) {
    echo "Temperature: {$temp}\n";

    $result = $tester->measureConsistency(
        prompt: 'Name the top 3 PHP frameworks',
        temperature: $temp,
        samples: 5
    );

    echo "  Unique responses: {$result['unique_count']}/5\n";
    echo "  Consistency score: " . round($result['consistency_score'], 1) . "%\n";
    echo "  Avg length: " . round($result['average_length'], 0) . " chars\n\n";
}

Troubleshooting

Error: "Call to undefined method messages()"

Symptom: Fatal error: Call to undefined method Anthropic\Anthropic::messages()

Cause: The client wasn't properly instantiated using the factory pattern.

Solution: Always use the factory to create the client:

php

// Wrong
$client = new Anthropic();

// Correct
$client = Anthropic::factory()
    ->withApiKey(getenv('ANTHROPIC_API_KEY'))
    ->make();

Problem: Inconsistent Outputs at Temperature 0.0

Symptom: Even with temperature => 0.0, outputs vary slightly between runs.

Cause: While temperature 0.0 is highly deterministic, some variation can occur due to:

Model updates from Anthropic
Context window differences
Floating-point precision in calculations

Solution: For absolute determinism, use temperature => 0.0 with top_p => 1.0 and consider caching results for identical inputs. For production data extraction, implement result validation and retry logic.

Error: "Temperature must be between 0 and 2"

Symptom: InvalidArgumentException: Temperature must be between 0 and 2

Cause: Temperature value outside the valid range (0.0 to 2.0).

Solution: Validate temperature before API calls:

php

if ($temperature < 0 || $temperature > 2) {
    throw new \InvalidArgumentException('Temperature must be between 0 and 2');
}

Problem: High Temperature Produces Nonsensical Output

Symptom: With temperature => 2.0, outputs are creative but sometimes incoherent or off-topic.

Cause: Very high temperature increases randomness, which can reduce coherence.

Solution: For creative tasks, use moderate temperature (1.3-1.5) with appropriate top_p (0.95). Combine with clear system prompts and constraints to maintain quality.

Exercises

Exercise 1: Temperature Optimizer

Build a system that automatically finds the optimal temperature for a given task based on requirements.

Requirements:

Accept task description and consistency requirements
Test multiple temperature values
Measure output quality and consistency
Return recommended temperature setting

Exercise 2: Multi-Strategy Generator

Create a content generator that uses different sampling strategies for different sections.

Requirements:

Use low temperature for factual sections
Use high temperature for creative sections
Combine outputs coherently
Track which strategy was used where

Exercise 3: Determinism Validator

Build a tool that validates whether responses are deterministic enough for production use.

Requirements:

Run same prompt multiple times
Compare outputs for consistency
Flag non-deterministic behavior
Recommend temperature adjustments

Solution Hints

For Exercise 1, create a test suite that runs prompts at different temperatures and scores results. For Exercise 2, parse content into sections and apply different strategies based on section type. For Exercise 3, use similarity scoring and statistical analysis to measure variance.

Wrap-up

Congratulations! You've mastered temperature and sampling parameters. Here's what you accomplished:

✓ Understood token-level sampling — You learned how Claude predicts and selects tokens
✓ Mastered temperature control — You can now configure Claude for deterministic (0.0) to creative (2.0) outputs
✓ Learned top-p and top-k — You understand how these parameters filter token choices
✓ Built practical tools — Created classes for data extraction, content generation, and code generation with optimal settings
✓ Implemented adaptive systems — Built assistants that automatically adjust parameters based on context
✓ Measured consistency — Created tools to validate deterministic behavior for production use
✓ Tested systematically — Built A/B testing frameworks to find optimal parameters
✓ Understood costs — Analyzed how sampling parameters affect token usage and costs
✓ Applied model-specific guidance — Learned optimal parameters for different Claude models

These sampling parameters are essential for production AI applications. Whether you're extracting structured data, generating creative content, or writing code, choosing the right parameters ensures Claude produces exactly the output you need.

Key Takeaways

✓ Temperature controls creativity vs determinism (0.0 = deterministic, 2.0 = creative)
✓ Top-p (nucleus sampling) filters tokens by cumulative probability
✓ Top-k limits consideration to k most probable tokens
✓ Combine temperature + top_p for fine-grained control
✓ Use temperature 0.0 for data extraction and structured output
✓ Use temperature 1.5+ for creative content generation
✓ Match sampling parameters to your use case requirements
✓ Test consistency for production deterministic tasks
✓ Use stop sequences with high temperature to control output length
✓ A/B test different configurations to find optimal settings
✓ Consider cost implications: higher temperature often means more tokens
✓ Apply model-specific guidelines for different Claude models

💻 Code Samples

All code examples from this chapter are available in the GitHub repository:

View Chapter 08 Code Samples

Clone and run locally:

bash

git clone https://github.com/dalehurley/codewithphp.git
cd codewithphp/code/claude-php/chapter-08
composer install
export ANTHROPIC_API_KEY="sk-ant-your-key-here"
php examples/02-temperature-comparison.php

Chapter 08: Temperature and Sampling Parameters ​

Overview ​

Prerequisites ​

What You'll Build ​

Objectives ​

How Language Model Sampling Works ​

Token Prediction Fundamentals ​

Temperature Parameter ​

What Temperature Controls ​

Temperature Scale Guide ​

Top-P (Nucleus Sampling) ​

Understanding Top-P ​

Top-P in Practice ​

Top-K Sampling ​

Understanding Top-K ​

Top-K Usage ​

Combining Sampling Parameters ​

Temperature + Top-P Interaction ​

Configuration Manager ​

Use Case Specific Configurations ​

Data Extraction (Deterministic) ​

Content Generation (Creative) ​

Code Generation (Focused) ​

Adaptive Temperature Based on Context ​

Dynamic Temperature Adjustment ​

Stop Sequences and Sampling Interaction ​

How Stop Sequences Affect Sampling ​

A/B Testing Sampling Parameters ​

Systematic Parameter Testing ​

Model-Specific Considerations ​

Optimal Parameters by Model ​

Performance and Cost Implications ​

Sampling Parameters and Token Usage ​

Measuring Output Consistency ​

Consistency Tester ​

Troubleshooting ​

Error: "Call to undefined method messages()" ​

Problem: Inconsistent Outputs at Temperature 0.0 ​

Error: "Temperature must be between 0 and 2" ​

Problem: High Temperature Produces Nonsensical Output ​

Exercises ​

Exercise 1: Temperature Optimizer ​

Exercise 2: Multi-Strategy Generator ​

Exercise 3: Determinism Validator ​

Wrap-up ​

Key Takeaways ​

Further Reading ​

💻 Code Samples ​

Chapter 08: Temperature and Sampling Parameters

Overview

Prerequisites

What You'll Build

Objectives

How Language Model Sampling Works

Token Prediction Fundamentals

Temperature Parameter

What Temperature Controls

Temperature Scale Guide

Top-P (Nucleus Sampling)

Understanding Top-P

Top-P in Practice

Top-K Sampling

Understanding Top-K

Top-K Usage

Combining Sampling Parameters

Temperature + Top-P Interaction

Configuration Manager

Use Case Specific Configurations

Data Extraction (Deterministic)

Content Generation (Creative)

Code Generation (Focused)

Adaptive Temperature Based on Context

Dynamic Temperature Adjustment

Stop Sequences and Sampling Interaction

How Stop Sequences Affect Sampling

A/B Testing Sampling Parameters

Systematic Parameter Testing

Model-Specific Considerations

Optimal Parameters by Model

Performance and Cost Implications

Sampling Parameters and Token Usage

Measuring Output Consistency

Consistency Tester

Troubleshooting

Error: "Call to undefined method messages()"

Problem: Inconsistent Outputs at Temperature 0.0

Error: "Temperature must be between 0 and 2"

Problem: High Temperature Produces Nonsensical Output

Exercises

Exercise 1: Temperature Optimizer

Exercise 2: Multi-Strategy Generator

Exercise 3: Determinism Validator

Wrap-up

Key Takeaways

Further Reading

💻 Code Samples