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Prompts Optimization

Prompt optimization system using DSPy modules and advanced prompt engineering techniques.

Overview​

The prompts optimization system provides comprehensive prompt engineering capabilities, including DSPy modules, prompt optimization, and performance tuning.

Core Features​

  • DSPy Modules: Advanced prompt optimization using DSPy
  • Prompt Engineering: Template-based prompt generation
  • Performance Tuning: Optimize prompts for better results
  • A/B Testing: Test different prompt variations
  • Quality Metrics: Measure prompt effectiveness

Usage Examples​

Basic Prompt Optimization​

from recoagent.prompts.optimization import PromptOptimizer

# Create prompt optimizer
optimizer = PromptOptimizer()

# Optimize prompt
optimized_prompt = optimizer.optimize_prompt(
    base_prompt="Answer this question: {question}",
    examples=[
        {"question": "What is AI?", "answer": "AI is artificial intelligence..."},
        {"question": "How does ML work?", "answer": "ML works by learning from data..."}
    ]
)

DSPy Integration​

from recoagent.prompts.dspy_modules import DSPyModuleManager

# Create DSPy module manager
dspy_manager = DSPyModuleManager()

# Create DSPy module
module = dspy_manager.create_module(
    name="qa_module",
    module_type="ChainOfThought",
    signature="question -> answer"
)

# Optimize module
optimized_module = dspy_manager.optimize_module(module, training_data)

API Reference​

PromptOptimizer Methods​

optimize_prompt(base_prompt: str, examples: List[Dict]) -> str​

Optimize prompt using examples

Parameters:

  • base_prompt (str): Base prompt template
  • examples (List[Dict]): Training examples

Returns: Optimized prompt

DSPyModuleManager Methods​

create_module(name: str, module_type: str, signature: str) -> DSPyModule​

Create DSPy module

Parameters:

  • name (str): Module name
  • module_type (str): Type of DSPy module
  • signature (str): Module signature

Returns: DSPyModule instance

See Also​