How does this skill replace dspy.Assert?

It uses dspy.Refine combined with a custom reward function to provide a more robust and supported way to handle constraints in DSPy 2.6+, following current best practices.

Does using this skill increase LLM API costs?

Yes, because both refinement and best-of-N strategies require multiple LLM calls to achieve higher output quality. It is recommended to limit attempts (N) to balance cost and performance.

What is the difference between dspy.Refine and dspy.BestOfN?

dspy.Refine attempts to improve a single output over multiple iterations by providing feedback, while dspy.BestOfN generates multiple independent candidates and selects the one with the highest reward score.

Can I use multiple constraints in a single reward function?

Yes, this skill demonstrates how to create comprehensive reward functions that weight different criteria—such as length, format, and content—into a single 0.0 to 1.0 score.

DSPy Output Refinement & Constraints

Name: DSPy Output Refinement & Constraints
Author: OmidZamani

byOmidZamani

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数据科学与机器学习

Enhances DSPy program reliability by enforcing custom constraints and reward functions through iterative refinement and best-of-N selection.

This skill provides a systematic approach to improving the quality and reliability of DSPy-based language model programs. It enables developers to implement complex validation logic using reward functions, replacing deprecated patterns like Assert and Suggest. By leveraging dspy.Refine for iterative corrections and dspy.BestOfN for selecting the highest-scoring candidate, it ensures that outputs adhere to specific formats, length constraints, and content requirements. This is essential for building production-grade RAG pipelines and structured data extraction systems where consistency is paramount.

主要功能

01Custom constraint validation for JSON structure, length, and keyword presence

02Iterative refinement using dspy.Refine for multi-attempt output correction

03Migration patterns for deprecated dspy.Assert and dspy.Suggest implementations

0431 GitHub stars

05High-quality output selection via dspy.BestOfN based on reward scoring

06Gradual scoring systems (0.0-1.0) for granular quality control

使用场景

01Improving RAG pipeline accuracy by scoring retrieved and generated content quality

02Validating and correcting complex JSON extractions from unstructured text

03Enforcing strict word counts and formatting rules for summary generation

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add omidzamani/dspy-skills dspy-output-refinement-constraints

For use in Claude.ai and ChatGPT

Download Skill