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 the architecture for your critical mineral supply chain risk assessment system. Detail your strategy for real-time news ingestion from sources like Mining.com and S&P Global, how you'll structure the data, and which APIs you'll use for market data.
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.
AI-Powered Critical Mineral Supply Chain Risk Analysis with WizardLM-2 & Optuna
The global energy transition relies heavily on critical minerals like copper, nickel, and cobalt, especially for EV batteries. However, their supply chains are vulnerable to geopolitical disputes, market volatility, and environmental concerns. This challenge requires participants to build an AI system that proactively identifies and quantifies risks in a critical mineral supply chain. The system will ingest real-time news and market data, analyze potential disruptions (e.g., policy changes, geopolitical events, production halts), and recommend mitigation strategies. The solution should focus on leveraging advanced NLP techniques to extract actionable insights from unstructured text and integrating an optimization framework to model supply chain resilience under various risk scenarios. The goal is to provide a predictive tool for commodity traders and supply chain managers to navigate complex global mineral markets effectively.
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.