dspy-output-refinement-constraints

# 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](../dspy-signature-designer/SKILL.md) - Optimize programs: [dspy-miprov2-optimizer](../dspy-miprov2-optimizer/SKILL.md) - Evaluate quality: [dspy-evaluation-suite](../dspy-evaluation-suite/SKILL.md) ## Inputs | Input | Type | Description | |-------|------|-------------| | `module` | `dspy.Module` | Module to refine | | `reward_fn` | `callable` | Constraint validation function | | `N` | `int` | Number of attempts | | `threshold` | `float` | Minimum reward to accept | ## Outputs | Output | Type | Description | |--------|------|-------------| | `refined_output` | `dspy.Prediction` | Validated, refined result | ## Workflow ### Phase 1: dspy.Refine for Iterative Improvement Refine iteratively improves outputs across multiple attempts: ```python 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 =...