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.
Design a user interface using Bito AI where a human user can submit market strategy questions to the CrewAI team. The Bito AI interface should display the team's progress, intermediate findings, and final strategic report, allowing for user feedback and iterative refinement.
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.
Autonomous Market Strategy Team
This challenge involves constructing an autonomous, multi-agent market strategy team using the CrewAI framework. The team will be tasked with analyzing the competitive landscape for emerging generative AI technologies, such as new video generation models or large language models like DeepSeek. The goal is to provide strategic recommendations for businesses looking to innovate or compete in this rapidly evolving sector. The core intelligence of the agent team will be driven by DeepSeek R1, selected for its advanced reasoning capabilities and cost-efficiency, dynamically routed via OpenRouter for optimal performance across various analytical tasks. Crucially, the agents will leverage MemGPT for long-term memory, allowing them to recall past analyses, adapt to evolving market trends, and maintain conversational context over extended interactions. This enables more sophisticated and consistent strategic planning. To support the development and interaction with this complex system, GitHub Copilot will serve as an AI engineering assistant for creating custom tools and refining agent logic, while Bito AI will provide a chat interface for users to query the team, review reports, and provide feedback, fostering a human-in-the-loop oversight.
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.