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Iterative Testing and DSPy Optimization

Inspect the original prompt language first, then copy or adapt it once you know how it fits your workflow.

Linked challenge: Agentic Code Optimization & Review

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Linked challenge
Agentic Code Optimization & Review

Prompt source

Original prompt text with formatting preserved for inspection.

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Test your agent system with various suboptimal code snippets. Evaluate the quality of the generated code, the accuracy of explanations, and the utility of the MCP tool feedback. Use DSPy's `Optimizer` or manual prompt refinement to improve your pipeline's performance. Document how iterative testing and prompt engineering (e.g., adding few-shot examples of good/bad code patterns) enhanced your agent's capabilities.

Adaptation plan

Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

Keep stable

Preserve the rubric, target behavior, and pass-fail criteria as the baseline for evaluation.

Tune next

Adjust fixtures, mocks, and thresholds to the system under test instead of weakening the assertions.

Verify after

Make sure the prompt catches regressions instead of just mirroring the happy-path examples.