Self-Refining Pipelines

Introduction

Self-refining pipelines automatically evaluate their own outputs and trigger refinement steps if quality criteria are not met. This is a powerful pattern for ensuring high-quality results.

The Refinement Loop

The core concept involves generating an initial result, evaluating it, and then iterating until a threshold is met or max iterations are reached.

class SelfRefiningModule(dspy.Module):
    def forward(self, x):
        output = self.generate(x)
        for _ in range(self.max_iterations):
            score = self.evaluate(output)
            if score > self.threshold:
                break
            output = self.refine(output, score)
        return output

Refinement Strategies

Strategies include:

• Incremental Refinement: Making small, targeted fixes.
• Rewrite: Completely regenerating the output based on feedback.
• Ensemble: Combining multiple different refinement approaches.

Example: Code Refiner

A self-refining system for code generation might check for syntax errors and style violations, then automatically fix them.

class CodeRefiner(dspy.Module):
    def forward(self, spec):
        code = self.generator(spec).code
        for _ in range(3):
            issues = self.analyzer(code)
            if not issues: break
            code = self.fixer(code, issues)
        return code