Operator-ready prompt for reuse, tuning, and workspace runs.
This item is set up for developers who want to inspect the original language, fork it into Workspace, and adapt the evidence model without losing the source prompt structure.
Implementation handoffs, eval setup, and prompt tuning where you need the original structure intact.
Inspect first, copy once, then fork into Workspace when you want variants, notes, and model settings attached to the same run.
Swap domain facts, examples, and any hard-coded entities for your own context.
Tighten the evidence or verification requirement if this is headed toward production.
Decide which failure mode you want to evaluate first before you branch the prompt.
This prompt already carries implementation detail, tool context, and a final-output instruction. Keep that structure intact when you tune it, or your comparison runs get noisy fast.
Open this prompt inside Workspace when you want a live iteration loop.
Copy for quick reuse, or run it in Workspace to keep prompt variants, model settings, and prompt-history changes in one place.
Structured source with 1 active lines to adapt.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Implement a hybrid reasoning approach for your ad generation system. For straightforward queries, use an 'instant' prompt for Gemini 2.5 Pro. For queries flagged as sensitive or complex (e.g., health-related, financial advice), trigger a 'deep think' mode that involves multiple reasoning steps, RAG lookups, and a self-correction loop. Provide pseudo-code or Python implementation for this conditional logic.
Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Hold the task contract and output shape stable so generated implementations remain comparable.
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.
Copy once for a pristine source snapshot, then move the prompt into Workspace when you want variants, run history, and side-by-side tuning without losing the original.
Prompt diagnostics
Quick signals for how structured this prompt already is and where adaptation work is likely to happen first.
This prompt is mostly narrative and instruction-driven, so you can adapt examples and output constraints first without disturbing the structure.
Gemini 2.5 Pro & DSPy for Contextual, Ethical AI Ad Generation
Following Google's rollout of sponsored content in AI answers, this challenge focuses on building an advanced AI system capable of generating contextually relevant and ethically compliant sponsored content. Leveraging Gemini 2.5 Pro's multimodal capabilities and long context window, you will design agents that understand user queries and generate ads that align with strict brand guidelines and ethical AI principles. DSPy will be used for programmatic prompting and optimization, ensuring high-quality, targeted output, while Semantic Kernel will facilitate integration into a mock content delivery platform. The system must demonstrate sophisticated hybrid reasoning, combining instant content generation for common queries with deeper reasoning for nuanced or sensitive topics. Crucially, it needs to implement robust RAG (Retrieval Augmented Generation) to access and adhere to a knowledge base of brand guidelines and ethical content policies, preventing misinformation or inappropriate advertising.
Use the challenge page to recover the original task boundaries before you tune the prompt. That keeps your variants grounded in the same evaluation target instead of drifting into a different problem.