dspy-bootstrap-fewshot

# DSPy Bootstrap Few-Shot Optimizer ## Goal Automatically generate and select optimal few-shot demonstrations for your DSPy program using a teacher model. ## When to Use - You have **10-50 labeled examples** - Manual example selection is tedious or suboptimal - You want demonstrations with reasoning traces - Quick optimization without extensive compute ## Related Skills - For more data (200+ examples): [dspy-miprov2-optimizer](../dspy-miprov2-optimizer/SKILL.md) - For agentic systems: [dspy-gepa-reflective](../dspy-gepa-reflective/SKILL.md) - Measure improvements: [dspy-evaluation-suite](../dspy-evaluation-suite/SKILL.md) ## Inputs | Input | Type | Description | |-------|------|-------------| | `program` | `dspy.Module` | Your DSPy program to optimize | | `trainset` | `list[dspy.Example]` | Training examples | | `metric` | `callable` | Evaluation function | | `metric_threshold` | `float` | Numerical threshold for accepting demos (optional) | | `max_bootstrapped_demos` | `int` | Max teacher-generated demos (default: 4) | | `max_labeled_demos` | `int` | Max direct labeled demos (default: 16) | | `max_rounds` | `int` | Max bootstrapping attempts per example (default: 1) | | `teacher_settings` | `dict` | Configuration for teacher model (optional) | ## Outputs | Output | Type | Description | |--------|------|-------------| | `compiled_program` | `dspy.Module` | Optimized program with demos | ## Workflow ### Phase 1: Setup ```python import dspy from dspy.teleprompt imp...