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DSPy for Prompt Optimization and Robustness

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

Linked challenge: AI Cyberattack Breakthrough Evaluator

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Linked challenge
AI Cyberattack Breakthrough Evaluator

Prompt source

Original prompt text with formatting preserved for inspection.

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0 checklist items
Integrate DSPy into your agent system to programmatically optimize the prompts used by both the 'Red Team Attacker' and 'Blue Team Defender'. Focus on tasks like 'vulnerability_detection' and 'mitigation_suggestion'. Define a signature for these tasks and use DSPy's optimizers (e.g., `BootstrapFewShot`) to make the agents' responses more robust and accurate in a security context. Document your DSPy program and optimization process.

Adaptation plan

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

Keep stable

Hold the task contract and output shape stable so generated implementations remain comparable.

Tune next

Update libraries, interfaces, and environment assumptions to match the stack you actually run.

Verify after

Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.