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.
Using the provided `AnalyzeMineralSupplyRisk` evaluation module, test your implemented AI agent. Focus on its accuracy in identifying risks, the relevance and quality of its justifications and recommendations, and its response latency. Based on the evaluation results, describe potential optimization strategies for prompt engineering, Milvus indexing, or Semantic Kernel skill design to improve performance and robustness. How would you handle conflicting information or novel geopolitical events?
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.
AI-Powered Critical Mineral Supply Chain Resilience with RAG and Vector DB
The global economy is increasingly reliant on critical minerals, but structural shortages, geopolitical tensions, and supply chain vulnerabilities pose significant risks. This challenge requires you to design and implement an AI-driven system to proactively identify, analyze, and mitigate potential disruptions in critical mineral supply chains. The system will ingest real-time news, economic data, and geopolitical intelligence to provide actionable insights for stakeholders in the mining and manufacturing sectors. Participants will build an AI agent that leverages Retrieval Augmented Generation (RAG) with Cohere Command R+ for advanced reasoning and Milvus as a high-performance vector database for semantic search. The agent, orchestrated by Semantic Kernel, will dynamically process vast amounts of unstructured and structured data, identify patterns, and generate risk assessments and mitigation strategies for minerals like copper and tungsten.
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.