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.
Design a DSPy pipeline that takes a user query and a brand focus as input and outputs a short, engaging sponsored ad. Your pipeline should include modules for: 1. Query analysis, 2. RAG-based guideline retrieval, and 3. Ad content generation using Gemini 2.5 Pro. Explain how you'd use DSPy's `dspy.Predict` and `dspy.Chain` (or `dspy.Program`) components.
Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Preserve the role framing, objective, and reporting structure so comparison runs stay coherent.
Swap in your own domain constraints, anomaly thresholds, and examples before you branch variants.
Check whether the prompt asks for the right evidence, confidence signal, and escalation path.
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.