Grok-2 & LangGraph: DMCA Violation Detection
Build an AI system to automatically detect potential DMCA violations on YouTube using Grok-2 and LangGraph. Grok-2's advanced reasoning and prompt engineering capabilities will be used to analyze video descriptions, comments, and metadata. LangGraph will create a graph-based workflow to manage the analysis process, linking videos to potential infringing content, and leveraging chain-of-thought prompting for detailed reasoning. The system should output a report identifying potential violations and providing supporting evidence.
What you are building
The core problem, expected build, and operating context for this challenge.
Build an AI system to automatically detect potential DMCA violations on YouTube using Grok-2 and LangGraph. Grok-2's advanced reasoning and prompt engineering capabilities will be used to analyze video descriptions, comments, and metadata. LangGraph will create a graph-based workflow to manage the analysis process, linking videos to potential infringing content, and leveraging chain-of-thought prompting for detailed reasoning. The system should output a report identifying potential violations and providing supporting evidence.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
What you should walk away with
Master LangGraph for building stateful DAG agent workflows.
Implement Grok-2 for advanced reasoning and prompt engineering.
Utilize chain-of-thought prompting techniques in Grok-2 for improved accuracy.
Design a graph-based workflow to manage the analysis and reporting processes.
Develop a system for extracting relevant information from YouTube videos.
Evaluate the system's performance using a test set of YouTube videos.
How this agent runs
Evaluate the system's accuracy in identifying DMCA violations and the clarity of its reports.
Challenge input
YouTube video URL
Agent execution
The configured agent processes the input under the challenge policy.
Evaluated output
JSON report identifying potential violations and providing supporting evidence.
- Checks if the system correctly identifies known violations.
- Precision target: 0.85
- Python execution harness
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- The evaluation module defines the checks.
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.
Run this agent on your dataset and AI stack
Bring your dataset, model providers, and success criteria. We will scope the right managed run for your team.
Scope a managed run[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
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