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DSPy Signature Implementation for Persona-Driven Responses

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Linked challenge: Adaptive Survey Bot Evasion with Gemini 2.5 Pro and DSPy

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Linked challenge

Adaptive Survey Bot Evasion with Gemini 2.5 Pro and DSPy

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Implement a DSPy `Signature` for generating survey responses that strictly adhere to a given persona. Include `Input` fields for the current question, the agent's persona description, and past conversation history. The `Output` should be the generated answer and potentially a confidence score. Demonstrate how you would use DSPy's `Optimizers` to refine this signature for persona consistency and natural language.

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

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

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

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

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This prompt is mostly narrative and instruction-driven, so adapt examples and output constraints before you rewrite the structure.

Linked challenge

Adaptive Survey Bot Evasion with Gemini 2.5 Pro and DSPy

Inspired by recent reports of AI agents bypassing bot detection, this challenge tasks developers with building a sophisticated LLM-based agent capable of completing online surveys while evading common bot detection heuristics. The agent will leverage Gemini 2.5 Pro's multimodal and 'Deep Think' capabilities for nuanced reasoning, combined with DSPy's programmatic prompting for optimizing its responses. Focus on generating human-like, contextually relevant answers and dynamically adapting the agent's persona to avoid detection, employing advanced techniques like extended thinking and adaptive reasoning budgets. The core of the challenge involves designing an agent architecture that can understand survey questions, maintain a consistent persona, and strategically vary its responses to appear non-automated. This requires integrating a 'critic' module that evaluates potential answers against bot detection patterns, allowing the agent to refine its output. Participants will explore the ethical implications of such agents while building a robust system.

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