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.
Using Claude Opus 4.1, generate a series of challenging and unexpected urban air mobility scenarios, including sensor failures, sudden obstacle appearances, and communication disruptions. Execute your eVTOL navigation system in these generated scenarios, analyze its performance, and describe any adjustments made to improve robustness based on the test results. Pay particular attention to how the system handles 'beyond visual line of sight' communication delays.
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.
Autonomous eVTOL Navigation with Multi-Sensor Fusion & Claude Opus for Design
The successful first flight of autonomous eVTOLs like Wisk's Gen 6 marks a pivotal step towards Urban Air Mobility (UAM). However, navigating complex, dynamic, and potentially contested urban airspaces presents significant challenges. This challenge focuses on designing and simulating an autonomous navigation and collision avoidance system for an eVTOL. The system must integrate data from multiple heterogeneous sensors (e.g., LiDAR, camera, radar, GNSS) to build a robust perception of the environment and make real-time, safe navigation decisions, even under conditions of communication delay or temporary denial. Participants will develop a solution that ensures safe flight paths, dynamic obstacle avoidance, and resilient operations in congested airspace. Emphasis will be placed on robust sensor fusion techniques, efficient path planning, and intelligent decision-making algorithms that can adapt to changing conditions. The project encourages leveraging Claude Opus 4.1 for high-level architectural design and complex scenario generation, NNI for optimizing navigation parameters, and Prefect for orchestrating simulation and data processing workflows.
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.