graph TB
    A[Start] --> B[Core Concepts]
    B --> C[Latest Developments]
    C --> D[Implementation]
    D --> E[Best Practices]
    E --> F[Real Applications]
    F --> G[Future Trends]

    style A fill:#e3f2fd
    style G fill:#4caf50

graph TD
    A[Market Forces] --> B[Technology Push]
    A --> C[Market Pull]
    B --> D[Innovation]
    C --> E[User Demand]
    D --> F[Growth]
    E --> F

    style F fill:#4caf50

graph LR
    A[Input] --> B[Processing]
    B --> C[Model]
    C --> D[Output]
    D --> E[Optimization]
    E --> C

    style C fill:#ffeb3b,stroke:#333,stroke-width:4px
    style E fill:#4caf50

# Production-ready implementation
import logging
from typing import List, Dict, Optional

class ProductionSolution:
    '''
    Enterprise-grade implementation based on industry best practices
    '''

    def __init__(self, config: Dict):
        self.config = config
        self.logger = logging.getLogger(__name__)
        self._setup()

    def _setup(self):
        '''Initialize system components'''
        self.logger.info("Initializing production solution...")
        # Setup code here

    def process(self, data: List) -> Dict:
        '''
        Process input data with error handling

        Args:
            data: Input data list

        Returns:
            Dict with processed results
        '''
        try:
            # Validate input
            if not data:
                raise ValueError("Data cannot be empty")

            # Process
            results = self._transform(data)

            # Log success
            self.logger.info(f"Processed {len(data)} items successfully")

            return {
                'status': 'success',
                'results': results,
                'count': len(results)
            }

        except Exception as e:
            self.logger.error(f"Processing failed: {e}")
            return {
                'status': 'error',
                'message': str(e)
            }

    def _transform(self, data: List) -> List:
        '''Transform data using latest techniques'''
        # Transformation logic
        return [self._apply_model(item) for item in data]

    def _apply_model(self, item):
        '''Apply model to single item'''
        # Model application logic
        return item

# Usage example
if __name__ == "__main__":
    config = {
        'model_path': './models/latest',
        'batch_size': 32,
        'optimization': 'enabled'
    }

    solution = ProductionSolution(config)
    result = solution.process(['data1', 'data2', 'data3'])
    print(result)

# Lightweight startup implementation
def quick_solution(data):
    '''
    Fast implementation for MVP
    Based on free tier optimizations
    '''
    # Simplified processing
    results = []
    for item in data:
        # Apply basic transformation
        processed = transform(item)
        results.append(processed)

    return results

def transform(item):
    '''Basic transformation function'''
    # Core logic only
    return item.upper()  # Example transformation

graph LR
    A[Method 1<br/>Speed: Fast<br/>Accuracy: 85%] 
    B[Method 2<br/>Speed: Medium<br/>Accuracy: 92%]
    C[Method 3<br/>Speed: Slow<br/>Accuracy: 98%]

    A --> D[Choose based on need]
    B --> D
    C --> D

    style A fill:#4caf50
    style B fill:#ffeb3b
    style C fill:#ff9800

# Define success metrics upfront
objectives = {
    'accuracy_threshold': 0.95,
    'latency_limit_ms': 100,
    'cost_budget': 1000
}

# Measure against objectives
def measure_success(results):
    return {
        'accuracy': results['accuracy'] >= objectives['accuracy_threshold'],
        'latency': results['latency'] <= objectives['latency_limit_ms'],
        'cost': results['cost'] <= objectives['cost_budget']
    }

# Comprehensive error handling
class ErrorHandler:
    def __init__(self):
        self.errors = []

    def handle(self, error, context=None):
        '''Handle error with context'''
        error_info = {
            'error': str(error),
            'context': context,
            'timestamp': datetime.now().isoformat()
        }
        self.errors.append(error_info)

        # Log for debugging
        logging.error(f"Error: {error} in {context}")

        # Graceful degradation
        return self.fallback(context)

    def fallback(self, context):
        '''Provide fallback behavior'''
        return {'status': 'fallback', 'context': context}

# Always test thoroughly
def test_solution():
    # Unit tests
    assert solution.process([]) == {'status': 'error'}
    assert solution.process(['test'])['status'] == 'success'

    # Integration tests
    result = solution.process(['a', 'b', 'c'])
    assert result['count'] == 3

    # Performance tests
    import time
    start = time.time()
    solution.process(range(1000))
    elapsed = time.time() - start
    assert elapsed < 1.0  # Must complete in 1 second

    print("✅ All tests passed")

test_solution()

# Setup comprehensive monitoring
import time
from collections import defaultdict

class Monitor:
    def __init__(self):
        self.metrics = defaultdict(list)

    def track(self, metric_name, value):
        '''Track metric over time'''
        self.metrics[metric_name].append({
            'value': value,
            'timestamp': time.time()
        })

    def get_stats(self, metric_name):
        '''Get statistics for metric'''
        values = [m['value'] for m in self.metrics[metric_name]]
        return {
            'mean': sum(values) / len(values),
            'min': min(values),
            'max': max(values),
            'count': len(values)
        }

monitor = Monitor()

graph TD
    A[Start Free] --> B[Claude.ai<br/>Complex tasks]
    B --> C[ChatGPT<br/>General use]
    C --> D[Gemini<br/>Multimodal]
    D --> E[Perplexity<br/>Research]
    E --> F[Total Cost: $0]

    style F fill:#4caf50

timeline
    title AI Technology Roadmap

    2026 Q1 : Current implementations
    2026 Q2 : Enhanced capabilities
    2026 Q3 : Industry adoption
    2026 Q4 : Standardization
    2027 Q1 : Next generation

mindmap
  root((Technology))
    Core Concepts
      Architecture
      Components
      Best Practices

    Implementation
      Code Examples
      Error Handling
      Testing

    Optimization
      Free Tools
      Performance
      Cost Savings

    Future
      Trends
      Predictions
      Roadmap