🔧 What is DSPy Compilation?
DSPy compilation transforms your high-level program into optimized prompts and weights. Unlike traditional compilation that converts source code to machine code, DSPy compilation optimizes the language model interactions within your program.
The compilation process includes:
Automatic Prompt Engineering
Crafting optimal prompts for your specific task
Example Selection
Choosing the best demonstrations for few-shot learning
Weight Tuning
Optimizing module parameters for better performance
Pipeline Optimization
Improving the overall program structure
🔄 The Compilation Pipeline
import dspy
from dspy.teleprompt import BootstrapFewShot
# Before compilation: High-level specification
class QASystem(dspy.Module):
def __init__(self):
super().__init__()
self.generate_answer = dspy.ChainOfThought("question -> answer")
def forward(self, question):
return self.generate_answer(question=question)
# Define metric
def answer_exact_match(example, pred, trace=None):
return example.answer.lower() == pred.answer.lower()
# After compilation: Optimized prompts and weights
optimized_qa = BootstrapFewShot(metric=answer_exact_match).compile(
QASystem(),
trainset=train_data
)⚙️ How Compilation Works
Program Specification
You define the high-level structure using DSPy modules
Training Data
Provide examples of inputs and desired outputs
Optimization Metric
Define how to measure performance (accuracy, F1, etc.)
Compilation
DSPy automatically optimizes using the specified optimizer
Evaluation
Test the compiled program on held-out data
📦 Types of Compilation
Prompt Compilation
Optimizes the natural language instructions:
- Rewrites instructions for clarity
- Adds relevant context
- Formats examples optimally
Example Compilation
Selects and orders training examples:
- Chooses diverse examples
- Orders by difficulty or relevance
- Balances different types of cases
Weight Compilation
Optimizes module parameters:
- Adjusts confidence thresholds
- Tunes generation parameters
- Optimizes module interactions
📊 Compilation vs Traditional Programming
| Traditional Programming | DSPy Compilation |
|---|---|
| Source code → Machine code | High-level LM program → Optimized prompts |
| Static optimization | Dynamic optimization based on data |
| One-time compilation | Iterative improvement possible |
| Hardware-specific | Task and data-specific |
| Manual optimization required | Automatic optimization |
🎯 When to Use Compilation
Use compilation when:
- You have training data available
- Performance is critical
- Task is complex or nuanced
- You want consistent results
- Manual prompt engineering is time-consuming
Skip compilation when:
- Task is very simple
- No training data available
- One-off tasks
- Rapid prototyping needed
💡 Compilation Best Practices
Start Simple
# Start with this
simple_classifier = dspy.Predict("text -> category")
# Then compile for better performance
optimized = BootstrapFewShot(metric=accuracy).compile(
simple_classifier,
trainset=data
)Use Sufficient Training Data
# Minimum 10-20 examples for basic tasks
# 50-100+ examples for complex tasks
# Diversity in examples is crucialChoose the Right Metric
# For classification: accuracy, F1
# For generation: ROUGE, BLEU
# For QA: exact match, F1
# Custom metrics for domain-specific tasksValidate Properly
# Split data properly
train_data, val_data = train_test_split(all_data, test_size=0.2)
# Compile on training data
compiled_program = optimizer.compile(program, trainset=train_data)
# Evaluate on validation data
results = evaluate(compiled_program, val_data)