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.
Design a series of sequential and collaborative tasks for your CrewAI agents to achieve the project goal of defining the AI model. Tasks should include 'Research Existing Solutions', 'Draft Architectural Concepts', 'Estimate Data Requirements', and 'Compile R&D Report'. Define your Crew, specifying how agents will communicate and execute tasks. Ensure tasks leverage the defined tools. Provide Python code for task and crew setup.
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.
R&D Team for Specialized AI Model Definition
Orchestrate a multi-agent team using CrewAI to simulate an R&D department tasked with defining the requirements and preliminary architecture for a highly specialized AI model, such as one for automating heavy construction equipment. This challenge requires defining distinct roles (e.g., AI Researcher, Robotics Engineer, Project Manager), assigning specific goals, and enabling collaborative problem-solving. Agents must leverage external tools for information gathering and document generation, ultimately producing a comprehensive R&D report detailing the model's purpose, key features, data needs, and architectural considerations. The focus is on complex task decomposition and inter-agent communication facilitated by a shared memory and structured output.
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.