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.
Create a Weaviate schema in Python for a class named 'MiningProject'. Include properties for 'projectName', 'commodityType', 'capexUSD', and 'geopoliticalRiskLevel'. Enable the 'text2vec-transformers' module for the description property.
Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Hold the task contract and output shape stable so generated implementations remain comparable.
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.
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 already mixes executable detail with instructions, so the safest path is to tune examples and interfaces before you rewrite the overall scaffold.
Predictive Mining Capital Allocation
With battery prices hitting record lows and commodity prices like gold and oil fluctuating, mining companies must decide which capital-intensive bulk projects to prioritize. This challenge tasks you with building a decision-support system that evaluates mining project viability. You will use the Mistral Nemo model for advanced multilingual analysis and TorchServe for deploying a custom price-prediction model. The core of the application will be a vector database that stores complex project profiles, including capital expenditure (Capex), geographical risks, and mineral types. You will implement a RAG pipeline that allows users to query which 'Megamines' are most resilient to a low-battery-price environment. Additionally, you will explore the use of the NNI (Neural Network Intelligence) toolkit to optimize the hyperparameters of a regression model predicting mineral price trends for 2026.
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.