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.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Using LangChain and LangGraph, define a stateful graph for a content verification system. Include at least three nodes: a 'Content Intake' node, an 'AIGenerationDetector' node (using Gemini 3 Flash), and a 'ComplianceChecker' node (also using Gemini 3 Flash). Add edges to route content from intake to detection, and then to compliance. Include a 'MemoryManager' node to interact with Cognee. Provide the basic Python code for defining the graph and its nodes, including LangGraph imports.
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.
Multi-Agent System for AI-Generated Content Verification & Compliance
Inspired by the 'Human Authored' logo initiative and growing concerns about AI-generated content, this challenge requires building a sophisticated multi-agent system using LangChain (specifically LangGraph for orchestration). The system will analyze content for authenticity, detect potential AI generation, and check for compliance against ethical guidelines. Utilizing Gemini 3 Flash for rapid analysis and summarization, the agent team will coordinate using graph-based workflows. Cognee will provide long-term memory for learning content patterns and historical decisions. Giskard will be integrated for continuous evaluation, bias detection, and governance, ensuring the system remains ethical and performs reliably. Coplay AI will serve as an interactive interface for users to submit content and receive detailed explanations of the analysis.
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.