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 2 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.
Refine the CrewAI workflow to handle a more complex misinformation scenario, potentially involving images (using GPT-4o's multimodal capabilities). For example, analyze a social media post containing both text and an image. The 'Content Analyzer' might need to interpret the image for context or manipulation. Adjust the task descriptions and delegation strategy so agents can pass information and refine tasks based on each other's findings. The 'Report Generator' should produce a comprehensive report citing specific sources retrieved by the 'Source Verifier'. How can agents effectively delegate and cross-verify tasks for robust fact-checking?
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.
Misinformation Debunking Team
In response to the pervasive issue of fabricated content and misdirection on social media, this challenge involves building a sophisticated multi-agent system using CrewAI. Your task is to design a team of specialized AI agents to collaboratively debunk misinformation, verify facts, and synthesize neutral, evidence-based reports. The team will be powered by OpenAI o4o for its multimodal reasoning and advanced tool-use capabilities. Each agent within the CrewAI team will have a distinct role (e.g., 'Source Verifier', 'Content Analyzer', 'Report Generator') and will utilize specific tools, including a vector database like Weaviate for rapid semantic search over verified knowledge bases. The system must be capable of processing social media content, identifying false claims, citing credible sources, and producing comprehensive reports, while its operational transparency and performance are monitored and evaluated through LangSmith.
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.