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.
Develop a logic within the multi-agent system where if the 'Fact-Checker' agent identifies a factual discrepancy, it triggers a conversation with the 'Consistency Analyst' to determine if this discrepancy also creates an internal inconsistency within the article. The final decision should be logged.
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.
OpenAI o3 Multi-Agent Content Verifier with AutoGen & Haystack
This challenge addresses the growing problem of easily falsifiable content by creating an advanced multi-agent system designed to verify the authenticity, consistency, and factual accuracy of written materials. Participants will build a team of specialized agents using AutoGen, orchestrated to collaborate on a content verification workflow. The system will leverage OpenAI o3 for sophisticated natural language understanding and reasoning, while Haystack will power robust Retrieval-Augmented Generation (RAG) over trusted knowledge bases and archived content to fact-check claims and identify inconsistencies. Agents will communicate using the A2A Protocol for structured information exchange, enabling a rigorous, automated editorial review process capable of uncovering subtle falsehoods or AI-generated manipulations, protecting against misinformation in critical applications.
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.