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name: dspy-optimization description: DSPy optimizers (teleprompters), evaluation metrics, and optimization workflows. Use when compiling programs with BootstrapFewShot, KNNFewShot, MIPROv2, GEPA, or defining custom metrics.

DSPy Optimization

DSPy optimizers, evaluation metrics, and optimization workflows.

Quick Start

Compile with BootstrapFewShot

Copyimport dspy

# Define program
program = dspy.ChainOfThought("question -> answer")

# Define teleprompter
teleprompter = dspy.BootstrapFewShot(max_labeled_demos=5)

# Compile with training data
trainset = [
    dspy.Example(question="What is 2+2?", answer="4"),
    dspy.Example(question="What is 3+3?", answer="6"),
]

compiled = teleprompter.compile(program, trainset=trainset)

# Use compiled program
result = compiled(question="What is 4+4?")
print(result.answer)

Optimize with MIPROv2

Copy# Initialize optimizer
teleprompter = dspy.MIPROv2(
    metric=accuracy_metric,
    auto="medium",  # light, medium, or heavy
)

# Optimize
compiled = teleprompter.compile(
    program,
    trainset=trainset
)

Use GEPA optimizer

Copy# Initialize GEPA with reflection LM
gepa = dspy.GEPA(
    metric=accuracy_metric,
    auto="medium",
    reflection_lm=dspy.LM("openai/gpt-4", temperature=1.0),
)

# Optimize with reflective prompt evolution
compiled = gepa.compile(program, trainset=trainset)

When to Use This Skill

Use this skill when:

• Compiling DSPy programs with teleprompters
• Choosing the right optimizer (BootstrapFewShot, KNNFewShot, MIPROv2, GEPA)
• Defining evaluation metrics (exact match, semantic similarity, custom)
• Running optimization workflows and evaluation
• Tuning optimizer parameters for better performance

Core Concepts

Teleprompters

Teleprompters automatically optimize DSPy programs by finding the best prompts and demonstrations.

Available optimizers:

• BootstrapFewShot: Few-shot learning with automatic demonstration generation
• KNNFewShot: Context-aware example selection using k-nearest neighbors
• LabeledFewShot: Uses only provided demonstrations
• MIPROv2: Advanced prompt tuning with multi-stage optimization
• GEPA: Reflective prompt evolution with LM-driven feedback

See: references/optimizers.md for:

• Detailed optimizer descriptions and parameters
• Comparison table of when to use each optimizer
• Best practices and configuration examples

Metrics

Metrics evaluate how well your DSPy program performs on test data.

Common metrics:

• exact_match: Exact string matching
• SemanticF1: Semantic overlap with decompositional mode
• Custom metrics: Domain-specific evaluation functions

See: references/metrics.md for:

• Metric implementation patterns
• Semantic similarity evaluation
• Multi-criteria metrics

Optimization Workflow

The optimization process:

1. Define program: Create your DSPy program
2. Define metric: Specify evaluation criteria
3. Choose optimizer: Select appropriate teleprompter
4. Compile: Run optimization on training data
5. Evaluate: Test on development set
6. Iterate: Tune parameters based on results

See: references/optimizers.md for optimization strategies.

Scripts

The scripts/ directory provides reusable tools for optimization:

• optimize-dspy.py: Run optimization with custom metrics
• test-signature.py: Validate signature structure

Progressive Disclosure

This skill uses progressive disclosure:

1. SKILL.md (this file): Quick reference and navigation
2. references/: Detailed technical docs loaded as needed

Related Skills

• dspy-basics: Signature design, basic modules, program composition
• dspy-advanced: ReAct agents, tool calling, output refinement
• dspy-configuration: LM setup, caching, and version management