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.
Using CrewAI, define a team of at least three agents (e.g., 'Market Analyst', 'Content Strategist', 'Cost Optimizer') with distinct roles, goals, and backstories for a generative AI media product. Describe how OpenAI Swarm could dynamically manage these agents. Outline their initial tasks and how they would collaborate to analyze product performance.
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.
Boost Generative AI Product Engagement
The challenge of user engagement and cost-effectiveness for innovative generative AI products is paramount. This challenge involves building a multi-agent system designed to act as a 'Product Growth Strategist' for a hypothetical generative AI media platform. The system will analyze market data, user engagement metrics, and content generation costs to identify root causes for low traction and high operational expenses. You will orchestrate a team of specialized agents using CrewAI and OpenAI Swarm, allowing them to collaborate, debate, and synthesize insights. Agents will leverage GPT-5 Pro for high-level strategic thinking and Claude Sonnet 4 for creative content analysis, accessing diverse data sources (market reports, user analytics, financial data) via RAG and integrating with simulated generative media APIs using MCP. The goal is to generate actionable, data-driven recommendations to improve user engagement and optimize costs for a cutting-edge generative AI product.
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.