name: dspy-output-refinement-constraints
version: "1.0.0"
dspy-compatibility: "3.1.2"
description: This skill should be used when the user asks to "refine DSPy outputs", "enforce constraints", "use dspy.Refine", "select best output", "use dspy.BestOfN", mentions "output validation", "constraint checking", "multi-attempt generation", "reward function", or needs to improve output quality through iterative refinement or best-of-N selection with custom constraints.
allowed-tools:
- Read
- Write
- Glob
- Grep

DSPy Output Refinement & Constraints

Goal

Improve output quality using iterative refinement (dspy.Refine) and best-of-N selection (dspy.BestOfN) with custom constraint validation.

When to Use

• Outputs need format validation (JSON, specific structure)
• Length constraints (max tokens, word count)
• Content requirements (must include X, avoid Y)
• Quality improvement through multiple attempts
• Replacing deprecated Assert/Suggest patterns

Related Skills

• Design signatures: dspy-signature-designer
• Optimize programs: dspy-miprov2-optimizer
• Evaluate quality: dspy-evaluation-suite

Inputs

Outputs

Workflow

Phase 1: dspy.Refine for Iterative Improvement

Refine iteratively improves outputs across multiple attempts:

import dspy

dspy.configure(lm=dspy.LM("openai/gpt-4o-mini"))

# Base module
summarizer = dspy.ChainOfThought("document -> summary: str")

# Reward function: checks constraints
def summary_reward(args, pred):
    summary = pred.summary
    word_count = len(summary.split())

    if word_count > 100 or len(summary) < 50:
        return 0.0
    if "important" not in summary.lower():
        return 0.5
    return 1.0

# Refine module
refined_summarizer = dspy.Refine(
    module=summarizer,
    reward_fn=summary_reward,
    N=3,
    threshold=1.0
)

# Use it
result = refined_summarizer(document="Long document text here...")
print(result.summary)

Phase 2: dspy.BestOfN for Selection

Generate N outputs and pick the best:

import dspy

def json_reward(args, pred):
    """Validate JSON format and fields."""
    import json
    try:
        data = json.loads(pred.output)
        if not {'name', 'age', 'email'}.issubset(data.keys()):
            return 0.3
        if '@' not in data.get('email', ''):
            return 0.5
        return 1.0
    except json.JSONDecodeError:
        return 0.0

# BestOfN: try 5 times, pick best
extractor = dspy.Predict("text -> output: str")
best_extractor = dspy.BestOfN(module=extractor, reward_fn=json_reward, N=5, threshold=1.0)

result = best_extractor(text="John Doe, 30 years old, [email protected]")
print(result.output)  # Best valid JSON

Phase 3: Multi-Constraint Reward Functions

Complex validation with scoring:

import dspy
import re

def comprehensive_reward(args, pred):
    """Validate format, length, and content."""
    text = pred.answer
    score = 0.0

    # Length: 50-150 words (33%)
    word_count = len(text.split())
    if 50 <= word_count <= 150:
        score += 0.33

    # Format: capitalized, ends with period (33%)
    if re.match(r'^[A-Z]', text) and text.endswith('.'):
        score += 0.33

    # Content: required terms present (34%)
    if all(term in text.lower() for term in ['data', 'analysis']):
        score += 0.34

    return score

# Use with Refine
qa = dspy.ChainOfThought("question -> answer: str")
refined_qa = dspy.Refine(module=qa, reward_fn=comprehensive_reward, N=4, threshold=0.9)

result = refined_qa(question="What is data science?")

Production Example

import dspy
import json
import logging

logger = logging.getLogger(__name__)

class StructuredExtractor(dspy.Module):
    """Extract structured data with validation."""

    def __init__(self):
        self.extractor = dspy.Predict(
            "text -> json_output: str"
        )
        self.refined = dspy.Refine(
            module=self.extractor,
            reward_fn=self.validation_reward,
            N=3,
            threshold=0.9
        )

    def validation_reward(self, args, pred):
        """Validate JSON structure and business logic."""
        try:
            data = json.loads(pred.json_output)
            score = 0.0

            # Required fields
            if {'product', 'price', 'quantity'}.issubset(data.keys()):
                score += 0.4

            # Type validation
            if isinstance(data.get('price'), (int, float)) and data['price'] > 0:
                score += 0.3
            if isinstance(data.get('quantity'), int) and data['quantity'] > 0:
                score += 0.3

            return score
        except (json.JSONDecodeError, TypeError) as e:
            logger.warning(f"Validation failed: {e}")
            return 0.0

    def forward(self, text: str):
        try:
            return self.refined(text=text)
        except Exception as e:
            logger.error(f"Extraction failed: {e}")
            return dspy.Prediction(json_output='{}')

# Usage
extractor = StructuredExtractor()
result = extractor(text="iPhone 15, $999, quantity: 50")
print(result.json_output)

Migration from Assert/Suggest

DSPy 2.6+ deprecates dspy.Assert/dspy.Suggest. Use Refine with reward functions:

# Old: dspy.Assert(len(output) < 100, "Too long")
# New:
def reward(args, pred):
    return 1.0 if len(pred.output) < 100 else 0.0

refined = dspy.Refine(module=module, reward_fn=reward, N=3, threshold=1.0)

Best Practices

1. Score gradually - Use 0.0-1.0 range, not binary pass/fail
2. Multiple constraints - Weight each constraint (e.g., 25% each for 4 checks)
3. Handle exceptions - Reward functions should never raise, return 0.0 on error
4. Limit attempts - 3-5 attempts for Refine, 5-10 for BestOfN
5. Log failures - Track which constraints fail most often

Limitations

• Each attempt costs an additional LLM call
• Reward functions don't receive feedback prompts (unlike GEPA)
• BestOfN is expensive (N × cost)
• No automatic constraint learning (manual reward design)
• Refine may not improve if base module is fundamentally wrong