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.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Design a CrewAI team for enterprise LLM strategy. Define at least three agent roles (e.g., 'LLM Market Analyst,' 'Technical Feasibility Expert,' 'Financial Impact Assessor') with their goals, tools, and backstories. Specify how Llama 3.3 70B and OpenAI o3 will be used across these agents for a hybrid reasoning approach. Outline a task hierarchy for generating an LLM strategy report.
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 role framing, objective, and reporting structure so comparison runs stay coherent.
Swap in your own domain constraints, anomaly thresholds, and examples before you branch variants.
Check whether the prompt asks for the right evidence, confidence signal, and escalation path.
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.
Enterprise LLM Strategy with Llama 3.3 and CrewAI MCP
The proliferation of LLM developers like Zhipu AI raising significant capital highlights the increasing demand for enterprise-grade LLM solutions. This challenge focuses on building a multi-agent system to inform an enterprise's LLM strategy, particularly around competitive intelligence, market trends, and internal deployment considerations. Developers will create a team of specialized agents to analyze the LLM market, assess strategic partnerships (like Meta's Manus deal), and recommend optimal LLM adoption pathways. The system will primarily utilize Llama 3.3 70B for its performance and fine-tuning potential, alongside OpenAI o3 for high-level synthesis and expert guidance. CrewAI will orchestrate role-based agent teams, each with distinct functions (e.g., Market Analyst, Technical Strategist, Financial Advisor). MCP tool integration will be central, allowing agents to securely access internal enterprise data, financial reports, and external market intelligence APIs, while Semantic Kernel will facilitate integration with existing enterprise applications and services.
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.