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.
Already linked to a challenge workflow.
Sign in to keep private prompt variations.
Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Simulate a scenario where an initial ROI projection for a data center project is deemed marginal (e.g., below a 10% target). Guide your Gemini 2.5 Pro-powered 'Decision Agent' to engage in extended thinking. The agent should dynamically allocate a higher reasoning budget to explore alternative energy procurement strategies or identify new governmental incentives. Provide output demonstrating the 'Decision Agent's' adaptive reasoning process and its refined recommendations.
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 rubric, target behavior, and pass-fail criteria as the baseline for evaluation.
Adjust fixtures, mocks, and thresholds to the system under test instead of weakening the assertions.
Make sure the prompt catches regressions instead of just mirroring the happy-path examples.
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.
Optimize AI Data Center ROI
The rapid global expansion of AI data centers presents immense investment opportunities but also significant ROI and risk management challenges. This challenge tasks you with building a sophisticated multi-agent system to analyze prospective data center projects. Your system will leverage Gemini 2.5 Pro for advanced strategic reasoning, utilizing its extended thinking capabilities to dissect complex financial models, regulatory landscapes, and energy market dynamics. The core of the system will be built with LangGraph, enabling robust, graph-based agent workflows that manage state, facilitate collaboration between specialized agents (e.g., Financial Analyst, Regulatory Expert), and allow for adaptive reasoning budgets. A critical component is the integration of the Model Context Protocol (Model Context Protocol) for secure and efficient tool integration, allowing agents to access and process mock enterprise data sources such as energy market APIs, local regulatory databases, and infrastructure cost repositories. The goal is to generate actionable investment recommendations and comprehensive risk assessments for new AI data center initiatives.
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.