school-of-prompt 0.3.0

🎸 Rock your prompts! Enterprise-grade prompt optimization with statistical rigor and production features

School of Prompt 🎸

Simple, powerful prompt optimization with minimal boilerplate.

Inspired by School of Rock - where every prompt can become a legend.

Quick Start

from school_of_prompt import optimize

# That's it! One function call to optimize prompts
results = optimize(
    data="band_reviews.csv",
    task="classify sentiment", 
    prompts=["How does this fan feel about our band?", "Is this review positive or negative?"],
    api_key="sk-..."
)

print(f"Best prompt: {results['best_prompt']}")
print(f"Accuracy: {results['best_score']:.2f}")

Installation

pip install school-of-prompt

Features

Features

🚀 Level 0: Dead Simple

Perfect for quick experiments and getting started.

results = optimize(
    data="band_reviews.csv",
    task="classify sentiment",
    prompts=["How do fans feel about this?", "Analyze sentiment"],
    api_key="sk-..."
)

🎛️ Level 1: Configuration-Driven

Enterprise-grade configuration with YAML files.

# Use YAML configuration for complex setups
results = optimize(config="youtube_age_rating.yaml")

# Or enhanced API with advanced features
results = optimize(
    data="student_performances.csv",
    task="rate performance from 1-10",
    prompts="prompts/performance_variants.txt",
    model={"name": "gpt-4", "temperature": 0.1},
    metrics=["mae", "within_1", "within_2", "valid_rate"],  # Advanced metrics
    sampling_strategy="stratified",  # Smart sampling
    cross_validation=True,  # Statistical rigor
    k_fold=5,
    cache_enabled=True,  # Production caching
    comprehensive_analysis=True,  # Deep insights
    api_key="sk-..."
)

🔧 Level 2: Enterprise & Multi-Dataset

Full enterprise features with custom implementations.

from school_of_prompt import optimize, CustomMetric
from school_of_prompt.data.registry import get_data_registry

# Register custom data enrichers
registry = get_data_registry()
registry.register_enricher("domain_features", extract_youtube_features)

# Multi-dataset enterprise workflow
results = optimize(
    data={
        "training": "datasets/youtube_train.csv",
        "validation": "datasets/youtube_val.csv", 
        "test": "datasets/youtube_test.csv"
    },
    task="rate age appropriateness from 0-18",
    prompts=["Age rating for: {title}", "Appropriate age: {title}"],
    metrics=["mae", "within_1", "within_2", "r2_score", "valid_rate"],
    enrichers=["text_length", "readability", "domain_features"],
    preprocessors=["clean_text", "normalize_labels"],
    cross_validation=True,
    comprehensive_analysis=True,
    parallel_evaluation=True,
    api_key="sk-..."
)

# Access advanced analysis
print(f"Statistical significance: {results['comprehensive_analysis']['statistical_significance']}")
print(f"Error patterns: {results['comprehensive_analysis']['error_analysis']}")
print(f"Recommendations: {results['comprehensive_analysis']['recommendations']}")

🏢 Enterprise Features

📊 Advanced Metrics & Evaluation

• Tolerance-based: within_1, within_2, within_3 for ±N accuracy
• Domain-specific: valid_rate, token_efficiency, response_quality
• Statistical: r2_score, prediction_confidence, error_std, median_error
• Significance testing: Paired t-tests between prompt variants
• Confidence intervals: Statistical confidence for all results

⚙️ Configuration-Driven Approach

# youtube_age_rating.yaml
task:
  name: "youtube_age_rating"
  type: "regression" 
  target_range: [0, 18]

datasets:
  training: "datasets/youtube_train.csv"
  validation: "datasets/youtube_val.csv"
  test: "datasets/youtube_test.csv"

evaluation:
  metrics: ["mae", "within_1", "within_2", "valid_rate"]
  sampling_strategy: "stratified"
  cross_validation: true
  k_fold: 5

cache:
  enabled: true
  expiry: "24h"

batch_processing:
  parallel_evaluation: true
  chunk_size: 100

🚀 Production-Ready Features

• Intelligent caching: 24h expiry, size management, LRU eviction
• Batch processing: Parallel evaluation with progress tracking
• Error handling: Retry logic, circuit breakers, graceful degradation
• Multi-dataset workflows: Training/validation/test dataset support
• Cross-validation: K-fold cross-validation for robust evaluation

🔍 Comprehensive Analysis

• Error pattern detection: Common errors, bias analysis, prediction patterns
• Performance breakdown: Analysis by category, difficulty, content length
• Statistical significance: Rigorous testing between prompt variants
• Actionable recommendations: Data-driven suggestions for improvement

Smart Defaults

The framework automatically handles common scenarios:

📊 Auto Data Loading & Enrichment

• CSV files: data="band_reviews.csv"
• JSONL files: data="performances.jsonl"
• DataFrames: data=my_dataframe
• Multi-datasets: data={"train": "train.csv", "test": "test.csv"}
• Custom sources: data=MyDataSource()
• Enrichment pipeline: Automatic text analysis, readability, sentiment features
• Preprocessing: Text cleaning, label normalization, deduplication
• Smart sampling: Random, stratified, and balanced sampling strategies

🎯 Auto Task Detection

• "classify sentiment" → Sentiment classification
• "rate from 1-10" → Performance rating task
• "categorize content" → Multi-class classification
• "generate summary" → Text generation

📏 Auto Metrics Selection

• Classification → Accuracy, F1-score, precision, recall, valid_rate
• Regression → MAE, RMSE, R²-score, within_1, within_2, within_3
• Generation → response_quality, token_efficiency, valid_rate
• All tasks → Automatic selection based on task type and target range

🤖 Auto Model Setup

• String: model="gpt-4"
• Config: model={"name": "gpt-4", "temperature": 0.1}
• Custom: model=MyModel()

Rock Star Examples

🎸 Band Review Sentiment Analysis

results = optimize(
    data="fan_reviews.csv",
    task="classify sentiment",
    prompts=[
        "How does this fan feel about our band performance?",
        "Is this review positive, negative, or neutral?",
        "Fan reaction analysis: {review}"
    ],
    api_key=os.getenv("OPENAI_API_KEY")
)

🥁 Student Performance Rating

results = optimize(
    data="student_performances.csv", 
    task="rate performance from 1-10",
    prompts=[
        "Rate this {instrument} performance from 1-10: {performance}",
        "As a rock teacher, how would you score this?",
        "School of Rock grade: {performance}"
    ],
    model="gpt-4",
    metrics=["mae", "accuracy"]
)

🛡️ Content Safety for Young Rockers

results = optimize(
    data="song_lyrics.csv",
    task="classify content as school-appropriate", 
    prompts="prompts/safety_check.txt",
    model={
        "name": "gpt-4",
        "temperature": 0.0,
        "max_tokens": 20
    },
    sample_size=500
)

🎬 Age Rating Classification

results = optimize(
    data="youtube_videos.csv",
    task="rate appropriate age from 0-18",
    prompts=[
        "What age is appropriate for: {title} - {description}",
        "Age rating for: {title}. Content: {description}",
        "Minimum age for this content: {title}"
    ],
    model="gpt-3.5-turbo",
    metrics=["mae", "accuracy"]
)

API Reference

optimize()

The main optimization function - rock your prompts!

Parameters:

• data (str|DataFrame|CustomDataSource): Your dataset
• task (str|CustomTask): Task description or custom task
• prompts (str|List[str]|Path): Prompt variants to test
• model (str|dict|CustomModel): Model configuration
• metrics (List[str]|List[CustomMetric]): Evaluation metrics
• api_key (str): API key (or set OPENAI_API_KEY env var)
• sample_size (int): Limit evaluation to N samples
• random_seed (int): For reproducible sampling
• output_dir (str): Save detailed results
• verbose (bool): Print progress

Returns:

{
    "best_prompt": "How does this fan feel about our band?",
    "best_score": 0.892,
    "prompts": {
        "prompt_1": {"scores": {"accuracy": 0.856, "f1_score": 0.834}},
        "prompt_2": {"scores": {"accuracy": 0.892, "f1_score": 0.889}}
    },
    "summary": {"metrics": {...}},
    "details": [...]
}

Environment Setup

# Set your API key
export OPENAI_API_KEY="sk-your-key-here"

# Or pass directly
results = optimize(..., api_key="sk-your-key-here")

Data Format

Your data should have:

• Input columns: Text or features to analyze
• Label column: Ground truth (named label, target, class, etc.)

CSV Example:

review,sentiment
"The band was amazing!",positive
"Terrible performance.",negative
"It was okay.",neutral

JSONL Example:

{"review": "The band was amazing!", "sentiment": "positive"}
{"review": "Terrible performance.", "sentiment": "negative"}

Extension Points

For advanced rockers who need custom behavior:

from school_of_prompt import CustomMetric, CustomDataSource, CustomModel, CustomTask
from school_of_prompt.data.registry import get_data_registry

# Custom metrics with domain-specific logic
class MyRockMetric(CustomMetric):
    name = "rock_factor"
    def calculate(self, predictions, actuals):
        return calculate_rock_awesomeness(predictions, actuals)

# Register custom data enrichers
registry = get_data_registry()
registry.register_enricher("rock_features", extract_rock_features)
registry.register_preprocessor("rock_cleaner", clean_rock_data)

# Custom data sources with enrichment
class MyDataSource(CustomDataSource):
    def load(self):
        return load_from_rock_database()

# Enterprise workflow with custom components
results = optimize(
    data=MyDataSource(),
    task=MyCustomTask(),
    prompts=dynamic_prompt_generator,
    metrics=[MyRockMetric(), "accuracy", "within_1"],
    enrichers=["rock_features", "text_length"],
    preprocessors=["rock_cleaner", "normalize_labels"],
    cross_validation=True,
    comprehensive_analysis=True,
    api_key="sk-..."
)

🎯 What's New in v0.3.0

✅ Completed Enterprise Features

• ✅ Advanced Metrics: Tolerance-based, domain-specific, statistical metrics
• ✅ Configuration System: Full YAML configuration support
• ✅ Production Features: Intelligent caching, batch processing, error handling
• ✅ Multi-Dataset Support: Training/validation/test workflows
• ✅ Cross-Validation: K-fold cross-validation with statistical rigor
• ✅ Comprehensive Analysis: Statistical significance, error patterns, recommendations
• ✅ Data Registry: Pluggable data sources and enrichment pipelines

🔮 Future Roadmap

• More Models: Anthropic Claude, local models, Azure OpenAI
• Auto Optimization: Genetic algorithms, Bayesian optimization
• Real-time Evaluation: Streaming evaluation for large datasets
• Advanced Visualizations: Interactive charts and dashboards

Contributing

We'd love your help! Rock on and contribute to make this even better.

License

MIT License. Rock freely! 🤘

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School of Prompt: Where prompts learn to rock! 🎸

"You're not hardcore unless you optimize hardcore!" - Dewey Finn (probably)