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.
Outline your approach to creating a multi-modal forecasting model for solar generation, wind generation, and local load demand, considering varying weather conditions (irradiance, wind speed, temperature) and historical consumption patterns. Explain how you will leverage Mistral Large 2 to integrate diverse data types and generate accurate predictions, including handling uncertainties. What specific data features will you prioritize for input, and how will you evaluate the model's performance for different forecast horizons?
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.
Real-time DER & Microgrid Optimization for Resilience with Advanced Forecasting
Design and implement an intelligent microgrid management system capable of real-time coordination of diverse Distributed Energy Resources (DERs), including solar PV, Battery Energy Storage Systems (BESS), and emerging technologies like networked geothermal. The system must prioritize grid resilience, especially during outages or critical events, while simultaneously optimizing for cost-efficiency and maximizing the integration of renewable energy sources. This challenge requires advanced forecasting of generation and demand, robust optimization for dynamic dispatch, and the ability to adapt to changing grid conditions. Participants will utilize cutting-edge Gen AI tools for multi-modal forecasting and real-time decision-making. The solution should demonstrate practical application by maintaining critical loads, minimizing operational costs, and enhancing the overall stability of a simulated microgrid under various operational and fault scenarios. The emphasis is on building a responsive and intelligent control architecture suitable for future smart grids and distributed energy landscapes.
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.