Meta-Prompt-Evolution-Hub

Scale-tested evolutionary prompt search (EPS) platform hosting tens of thousands of prompts with evaluation scores. Integrates with Eval-Genius and Async-Toolformer-Orchestrator for continuous A/B testing and prompt optimization at scale.

Overview

Meta-Prompt-Evolution-Hub uses evolutionary algorithms to automatically discover, optimize, and maintain high-performing prompts across diverse tasks. The platform continuously evolves prompt populations through mutation, crossover, and selection based on real-world performance metrics.

Key Features

• Massive Scale: Handle 100K+ prompts with distributed evaluation
• Evolutionary Optimization: Genetic algorithms for prompt discovery
• Continuous A/B Testing: Real-time performance tracking
• Multi-Objective: Optimize for accuracy, cost, latency, and safety
• Version Control: Git-like prompt versioning and branching
• Auto-Documentation: Self-documenting prompt genealogy

Installation

# Basic installation
pip install meta-prompt-evolution-hub

# With distributed computing
pip install meta-prompt-evolution-hub[distributed]

# With all evaluation frameworks
pip install meta-prompt-evolution-hub[full]

# Development installation
git clone https://github.com/yourusername/meta-prompt-evolution-hub
cd meta-prompt-evolution-hub
pip install -e ".[dev]"

Quick Start

Basic Prompt Evolution

from meta_prompt_evolution import EvolutionHub, PromptPopulation

# Initialize evolution hub
hub = EvolutionHub(
    population_size=1000,
    generations=50,
    mutation_rate=0.1,
    crossover_rate=0.7
)

# Create initial population
population = PromptPopulation.from_seeds([
    "You are a helpful assistant. {task}",
    "As an AI assistant, I will {task}",
    "Let me help you with {task}"
])

# Define fitness function
def fitness_function(prompt, test_cases):
    scores = []
    for case in test_cases:
        response = llm(prompt.format(task=case.task))
        score = evaluate_response(response, case.expected)
        scores.append(score)
    return np.mean(scores)

# Evolve population
evolved_population = hub.evolve(
    population=population,
    fitness_fn=fitness_function,
    test_cases=test_dataset,
    selection_method="tournament"
)

# Get best prompts
best_prompts = evolved_population.get_top_k(10)

Continuous A/B Testing

from meta_prompt_evolution import ABTestOrchestrator

# Set up A/B testing
ab_tester = ABTestOrchestrator(
    production_endpoint="https://api.example.com",
    metrics=["accuracy", "latency", "user_satisfaction"],
    confidence_level=0.95
)

# Deploy prompt variants
variants = {
    "control": current_production_prompt,
    "variant_a": evolved_prompts[0],
    "variant_b": evolved_prompts[1]
}

ab_tester.deploy_test(
    variants=variants,
    traffic_split=[0.5, 0.25, 0.25],
    duration_hours=24,
    min_samples=10000
)

# Monitor results
results = ab_tester.get_results()
if results.variant_a.is_significant_improvement():
    ab_tester.promote_to_production("variant_a")

Architecture

meta-prompt-evolution-hub/
├── meta_prompt_evolution/
│   ├── evolution/
│   │   ├── algorithms/     # Evolutionary algorithms
│   │   ├── operators/      # Mutation, crossover operators
│   │   ├── selection/      # Selection strategies
│   │   └── diversity/      # Diversity maintenance
│   ├── evaluation/
│   │   ├── metrics/        # Evaluation metrics
│   │   ├── benchmarks/     # Standard benchmarks
│   │   ├── distributed/    # Distributed evaluation
│   │   └── caching/        # Result caching
│   ├── optimization/
│   │   ├── multi_objective/# Pareto optimization
│   │   ├── constraints/    # Safety constraints
│   │   └── adaptive/       # Adaptive parameters
│   ├── storage/
│   │   ├── database/       # Prompt database
│   │   ├── versioning/     # Version control
│   │   └── indexing/       # Efficient search
│   ├── deployment/
│   │   ├── ab_testing/     # A/B test orchestration
│   │   ├── monitoring/     # Performance monitoring
│   │   └── rollback/       # Safety mechanisms
│   └── analysis/
│       ├── genealogy/      # Prompt lineage
│       ├── clustering/     # Prompt clustering
│       └── insights/       # Pattern discovery
├── web_ui/                 # Web interface
├── cli/                   # Command-line tools
└── examples/              # Example configurations

Evolutionary Algorithms

Advanced Evolution Strategies

from meta_prompt_evolution.evolution import (
    NSGA2, CMA_ES, MAP_Elites, 
    NoveltySearch, QualityDiversity
)

# Multi-objective optimization with NSGA-II
nsga2 = NSGA2(
    objectives=["accuracy", "brevity", "clarity"],
    population_size=500
)

pareto_front = nsga2.evolve(
    initial_population=population,
    generations=100,
    mutation_ops=[
        "word_substitution",
        "sentence_reordering",
        "instruction_injection"
    ]
)

# MAP-Elites for diversity
map_elites = MAP_Elites(
    behavior_dimensions=["formality", "specificity", "length"],
    grid_resolution=20
)

diverse_prompts = map_elites.evolve(
    population=population,
    niche_size=10,
    generations=200
)

Custom Mutation Operators

from meta_prompt_evolution.operators import MutationOperator

@MutationOperator.register("semantic_mutation")
class SemanticMutation:
    def __init__(self, similarity_threshold=0.8):
        self.embedder = SentenceTransformer('all-mpnet-base-v2')
        self.threshold = similarity_threshold
    
    def mutate(self, prompt):
        # Extract key concepts
        concepts = extract_concepts(prompt)
        
        # Find semantically similar replacements
        mutations = []
        for concept in concepts:
            similar = find_similar_terms(concept, self.threshold)
            mutated = prompt.replace(concept, random.choice(similar))
            mutations.append(mutated)
        
        return random.choice(mutations)

# Use custom operator
hub.add_mutation_operator(
    SemanticMutation(),
    probability=0.3
)

Distributed Evaluation

Parallel Fitness Evaluation

from meta_prompt_evolution.distributed import DistributedEvaluator
import ray

# Initialize Ray cluster
ray.init(address="ray://head-node:10001")

# Distributed evaluator
evaluator = DistributedEvaluator(
    num_workers=100,
    batch_size=50,
    timeout=30  # seconds per evaluation
)

# Parallel evaluation
@ray.remote
def evaluate_prompt(prompt, test_batch):
    results = []
    for test in test_batch:
        result = run_evaluation(prompt, test)
        results.append(result)
    return results

# Evaluate entire population
fitness_scores = evaluator.evaluate_population(
    population=population,
    test_suite=large_test_suite,
    cache_results=True
)

Async Evaluation Pipeline

from meta_prompt_evolution.evaluation import AsyncEvaluator

# Continuous evaluation pipeline
async_eval = AsyncEvaluator(
    models=["gpt-4", "claude-2", "llama-70b"],
    rate_limits={"gpt-4": 100, "claude-2": 50, "llama-70b": 200}
)

# Stream evaluations
async def continuous_evolution():
    async for generation in hub.evolve_async(population):
        # Evaluate in parallel across models
        scores = await async_eval.evaluate_generation(generation)
        
        # Update population based on scores
        population = await hub.select_next_generation(
            generation,
            scores,
            elitism_rate=0.1
        )
        
        # Checkpoint best prompts
        if generation.number % 10 == 0:
            await hub.checkpoint(generation.best_prompts(100))

Prompt Database

Storage and Indexing

from meta_prompt_evolution.storage import PromptDatabase

# Initialize database
db = PromptDatabase(
    backend="postgresql",
    connection_string="postgresql://localhost/prompts"
)

# Store prompt with metadata
db.store_prompt(
    prompt=evolved_prompt,
    metadata={
        "generation": 42,
        "fitness_scores": {"accuracy": 0.95, "latency": 120},
        "parent_ids": [parent1.id, parent2.id],
        "mutations_applied": ["semantic_shift", "length_reduction"],
        "test_results": detailed_results
    }
)

# Query prompts
high_performing = db.query(
    filters={
        "fitness_scores.accuracy": {"$gt": 0.9},
        "fitness_scores.latency": {"$lt": 150},
        "generation": {"$gte": 30}
    },
    order_by="fitness_scores.accuracy",
    limit=50
)

# Semantic search
similar_prompts = db.semantic_search(
    query="explain quantum computing simply",
    k=20,
    threshold=0.85
)

Version Control

from meta_prompt_evolution.versioning import PromptVersionControl

# Git-like version control
vcs = PromptVersionControl(db)

# Create branch for experimentation
branch = vcs.create_branch("quantum_explanation_v2")

# Commit evolved prompt
commit = vcs.commit(
    prompt=new_prompt,
    message="Improved clarity for technical concepts",
    parent_commit=previous_commit_hash
)

# Merge successful evolution
if new_prompt.fitness > baseline.fitness:
    vcs.merge(
        source_branch="quantum_explanation_v2",
        target_branch="main",
        strategy="fitness_weighted"
    )

# Roll back if needed
if production_issues_detected:
    vcs.rollback(to_commit=last_stable_commit)

Multi-Objective Optimization

Pareto Front Exploration

from meta_prompt_evolution.optimization import ParetoOptimizer

# Define objectives
objectives = {
    "accuracy": "maximize",
    "cost": "minimize",
    "latency": "minimize",
    "safety_score": "maximize"
}

# Pareto optimization
pareto_opt = ParetoOptimizer(
    objectives=objectives,
    constraint_functions={
        "min_accuracy": lambda x: x["accuracy"] >= 0.8,
        "max_cost": lambda x: x["cost"] <= 0.01,
        "safety_threshold": lambda x: x["safety_score"] >= 0.95
    }
)

# Find Pareto-optimal prompts
pareto_prompts = pareto_opt.optimize(
    population=population,
    generations=100,
    archive_size=1000
)

# Interactive selection
selected_prompt = pareto_opt.interactive_selection(
    pareto_prompts,
    preference_weights={
        "accuracy": 0.4,
        "cost": 0.3,
        "latency": 0.2,
        "safety_score": 0.1
    }
)

Analysis and Insights

Prompt Genealogy

from meta_prompt_evolution.analysis import GenealogyAnalyzer

analyzer = GenealogyAnalyzer(db)

# Trace prompt lineage
lineage = analyzer.trace_lineage(
    prompt_id=best_prompt.id,
    generations_back=50
)

# Visualize evolution tree
lineage.visualize(
    color_by="fitness_score",
    node_size_by="usage_count",
    save_path="prompt_evolution_tree.html"
)

# Identify breakthrough mutations
breakthroughs = analyzer.find_breakthrough_moments(
    metric="accuracy",
    improvement_threshold=0.1
)

for breakthrough in breakthroughs:
    print(f"Generation {breakthrough.generation}:")
    print(f"  Mutation: {breakthrough.mutation}")
    print(f"  Improvement: {breakthrough.improvement:.2%}")

Pattern Discovery

from meta_prompt_evolution.analysis import PatternMiner

miner = PatternMiner()

# Find successful prompt patterns
patterns = miner.extract_patterns(
    successful_prompts=top_1000_prompts,
    min_support=0.1,
    min_confidence=0.8
)

# Common elements in high-performing prompts
common_elements = miner.find_common_elements(
    prompts=high_accuracy_prompts,
    element_types=["instructions", "formatting", "examples"]
)

# Cluster prompts by strategy
clusters = miner.cluster_by_strategy(
    prompts=all_prompts,
    n_clusters=10,
    features=["syntax", "semantics", "structure"]
)

Production Deployment

Continuous Deployment Pipeline

from meta_prompt_evolution.deployment import ContinuousDeployment

cd_pipeline = ContinuousDeployment(
    staging_env="https://staging.api.com",
    production_env="https://api.com",
    rollout_strategy="canary"
)

# Automated deployment pipeline
@cd_pipeline.on_new_champion
async def deploy_new_champion(champion_prompt):
    # Run safety checks
    safety_passed = await cd_pipeline.safety_checks(
        prompt=champion_prompt,
        checks=["toxicity", "hallucination", "instruction_following"]
    )
    
    if not safety_passed:
        return False
    
    # Canary deployment
    await cd_pipeline.canary_deploy(
        prompt=champion_prompt,
        initial_traffic=0.01,
        ramp_up_hours=24,
        success_criteria={
            "error_rate": {"max": 0.001},
            "p95_latency": {"max": 500},
            "user_satisfaction": {"min": 4.5}
        }
    )
    
    return True

Monitoring and Alerting

from meta_prompt_evolution.monitoring import PromptMonitor

monitor = PromptMonitor(
    metrics_backend="prometheus",
    alerting_backend="pagerduty"
)

# Set up monitoring
monitor.track_metrics({
    "prompt_performance": ["accuracy", "f1_score", "latency"],
    "user_metrics": ["satisfaction", "task_completion", "retry_rate"],
    "system_metrics": ["token_usage", "cost", "error_rate"]
})

# Define alerts
monitor.add_alert(
    name="performance_degradation",
    condition="accuracy < 0.85 for 5 minutes",
    severity="high",
    action="rollback_to_previous"
)

monitor.add_alert(
    name="cost_spike",
    condition="hourly_cost > 100",
    severity="medium",
    action="switch_to_efficient_variant"
)

Web Interface

Dashboard

from meta_prompt_evolution.web import Dashboard

# Launch web dashboard
dashboard = Dashboard(
    hub=hub,
    port=8080,
    auth="oauth2"
)

# Custom visualizations
dashboard.add_visualization(
    "evolution_progress",
    type="real_time_chart",
    metrics=["best_fitness", "population_diversity", "mutation_rate"]
)

dashboard.add_visualization(
    "prompt_explorer",
    type="interactive_graph",
    data="prompt_genealogy"
)

dashboard.launch()

Best Practices

Population Diversity

from meta_prompt_evolution.diversity import DiversityMaintainer

# Maintain healthy diversity
diversity_maintainer = DiversityMaintainer(
    min_diversity=0.3,
    diversity_metric="embedding_distance"
)

# Inject diversity when needed
@hub.on_generation_complete
def maintain_diversity(generation):
    diversity = diversity_maintainer.calculate_diversity(generation)
    
    if diversity < diversity_maintainer.min_diversity:
        # Introduce novel prompts
        novel_prompts = diversity_maintainer.generate_novel_prompts(
            num_prompts=50,
            methods=["random_generation", "crossover_with_archive", "llm_generation"]
        )
        generation.inject_prompts(novel_prompts)

Evaluation Efficiency

from meta_prompt_evolution.evaluation import EfficientEvaluator

# Reduce evaluation costs
efficient_eval = EfficientEvaluator(
    surrogate_model="distilgpt2",  # Fast approximation
    full_model="gpt-4",            # Accurate but expensive
    surrogate_confidence_threshold=0.9
)

# Adaptive evaluation
def adaptive_fitness(prompt):
    # Quick surrogate evaluation
    surrogate_score, confidence = efficient_eval.surrogate_eval(prompt)
    
    # Full evaluation only when uncertain
    if confidence < efficient_eval.surrogate_confidence_threshold:
        return efficient_eval.full_eval(prompt)
    
    return surrogate_score

Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

Citation

@software{meta_prompt_evolution_hub,
  title={Meta-Prompt-Evolution-Hub: Evolutionary Prompt Optimization at Scale},
  author={Daniel Schmidt},
  year={2025},
  url={https://github.com/danieleschmidt/meta-prompt-evolution-hub}
}

License

MIT License - see LICENSE for details.

Acknowledgments

• Evolutionary computation community
• LLM evaluation frameworks
• Open-source contributors