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Implement DSPy Prompt Optimization

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

Linked challenge: Adaptive Reasoning with Gemini 3 Flash: A LangGraph MCP Agent for Cost-Optimized Analytics

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Linked challenge
Adaptive Reasoning with Gemini 3 Flash: A LangGraph MCP Agent for Cost-Optimized Analytics

Prompt source

Original prompt text with formatting preserved for inspection.

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Develop DSPy signatures and programmatic pipelines for one of your agent's core reasoning steps (e.g., 'summarize_market_report'). Your implementation should specifically target leveraging Gemini 3 Flash's capabilities while aiming for 'Pro-grade reasoning'. Include considerations for few-shot examples and self-reflection steps to enhance output quality.

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.