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.
Configure the 'Economic Forecaster' agent to use Claude Sonnet 4 to analyze the economic implications of the proposed policy shift. It should query the LlamaIndex RAG system for relevant data and then synthesize a short-term and long-term economic forecast, including market shifts and supply chain effects.
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 is mostly narrative and instruction-driven, so you can adapt examples and output constraints first without disturbing the structure.
Geo-Economic Impact Analysis with LlamaIndex & Langroid Agents
The discussion around Nvidia's H200 chips to China highlights the intricate web of geopolitics and economics. This challenge requires you to develop a multi-agent system capable of analyzing and forecasting the complex geopolitical and economic impacts of hypothetical policy shifts, such as changes in tech export controls. You will use Langroid for rapid prototyping and deployment of specialized agents and LlamaIndex for advanced Retrieval-Augmented Generation (RAG). Agents will ingest and process diverse data sources including news articles, policy documents, and economic reports. Utilizing Claude Sonnet 4 for rapid information synthesis and LlamaIndex's hybrid RAG capabilities, the system will model potential market reactions, supply chain shifts, and diplomatic consequences. The challenge emphasizes multi-agent debate and adaptive thinking budgets to refine analyses and identify key drivers of change.
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.