The Challenge of Limited Data
Many real-world scenarios require training with severely limited labeled data. DSPy provides a comprehensive framework for building minimal data training pipelines that combine multiple optimization strategies.
Core principles: Data Efficiency, Strategy Diversity, Robust Validation, Confidence Awareness, and Adaptability.
Pipeline Architecture
class MinimalDataTrainingPipeline:
def __init__(self, config):
self.config = config # num_examples, task_type, strategies
def execute_pipeline(self, base_program, examples):
# Stage 1: Data Analysis
data_analysis = self._analyze_training_data(examples)
# Stage 2: Strategic Data Augmentation
augmented_data = self._augment_data(examples)
# Stage 3: Multi-Strategy Optimization
optimized = self._optimize(base_program, augmented_data)
# Stage 4: Robust Validation
validated = self._validate(optimized)
return validated