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 system architecture that uses Gemini 2.5 Pro for multimodal generation, DSPy for prompt optimization, and LlamaIndex for RAG. Outline the data flow and interaction points between these components for generating marketing assets based on a textual brief and brand guidelines.
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.
Multimodal Asset Generation
This challenge involves building an advanced generative AI system capable of producing creative marketing assets, including images with embedded text, based on complex briefs and brand guidelines. Leveraging the multimodal capabilities of Gemini 3 and Nano Banana Pro, participants will orchestrate a workflow that not only generates visually compelling images but also ensures accurate and contextually relevant text rendering directly within the image. The core of this challenge lies in integrating prompt optimization techniques using DSPy with sophisticated knowledge retrieval via LlamaIndex. This hybrid approach enables the system to dynamically adapt prompts for Gemini 3 and Nano Banana Pro, ensuring adherence to brand style guides and creative objectives fetched through RAG, while also self-correcting for improved text fidelity and image quality. This system will simulate a creative agency assistant, transforming abstract marketing concepts into concrete visual outputs.
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.