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COPRO (Collaborative Prompt Optimizer) is a simple but effective optimizer that iteratively refines prompts through generation and evaluation cycles.

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How it Works

COPRO uses a straightforward approach:
  1. Generate candidates: Create multiple prompt variations using an LLM
  2. Evaluate: Test each candidate on your training data
  3. Refine: Use the best candidates to generate improved versions
  4. Repeat: Continue for a fixed number of depth iterations

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Configuration

let copro = COPRO::builder()
    .breadth(10)              // Number of candidates per iteration
    .depth(3)                 // Number of refinement iterations
    .init_temperature(1.4)    // Temperature for generation
    .track_stats(false)       // Track optimization statistics
    .build();

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Usage Example

use dspy_rs::{COPRO, Optimizer};

#[derive(Builder, Optimizable)]
struct MyModule {
    #[parameter]
    predictor: Predict,
}

impl Module for MyModule {
    async fn forward(&self, inputs: Example) -> Result<Prediction> {
        self.predictor.forward(inputs).await
    }
}

impl Evaluator for MyModule {
    async fn metric(&self, example: &Example, prediction: &Prediction) -> f32 {
        // Your evaluation logic
        if prediction.get("answer", None) == example.get("expected", None) {
            1.0
        } else {
            0.0
        }
    }
}

#[tokio::main]
async fn main() -> Result<()> {
    let lm = LM::builder()...build();
    configure(lm, ChatAdapter);
    
    let mut module = MyModule::builder()...build();
    
    let copro = COPRO::builder()
        .breadth(10)
        .depth(3)
        .build();
    
    copro.compile(&mut module, trainset).await?;
    
    Ok(())
}

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When to Use COPRO

Best for:
  • Quick iteration cycles
  • Simple tasks
  • Limited compute budget
  • When you need results fast
Avoid when:
  • You need best possible quality (use MIPROv2 or GEPA)
  • Task has complex failure modes (use GEPA)
  • You want to leverage prompting best practices (use MIPROv2)

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Comparison with Other Optimizers

FeatureCOPROMIPROv2GEPA
SpeedFastSlowMedium
QualityGoodBetterBest
FeedbackScoreScoreScore + Text
DiversityLowMediumHigh
SetupSimpleModerateModerate

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Configuration Details

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Breadth

Number of candidate prompts generated at each iteration. Higher breadth means more exploration but more compute. Recommended: 5-15

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Depth

Number of refinement iterations. Each iteration builds on the best candidates from the previous one. Recommended: 2-5

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Temperature

Controls randomness in prompt generation. Higher temperature means more diverse candidates. Recommended: 1.0-1.5

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Track Stats

When enabled, COPRO tracks detailed statistics about all evaluated candidates and their scores over time.

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Implementation Notes

COPRO maintains:
  • All evaluated candidates with their scores
  • Best candidates from each iteration
  • History of improvements over iterations
The algorithm avoids re-evaluating candidates it has already seen by caching results.

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Examples

COPRO Examples

See examples 03-evaluate-hotpotqa.rs and 04-optimize-hotpotqa.rs
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