Graph-Based Legal AI for Compliance
This challenge focuses on building a cutting-edge, graph-based legal research and compliance assistant. You will design a system that can autonomously interpret complex legal documents, identify relevant case law, and assess compliance risks using advanced generative AI models. The emphasis will be on creating a robust, explainable, and iterative reasoning process.
What you are building
Build a graph-based multi-agent system using LangGraph with GPT-5 Pro for autonomous legal research and compliance, leveraging extended thinking and RAG.
How work is evaluated
The system will be evaluated on its ability to accurately answer complex legal queries, identify compliance risks in given scenarios, and provide well-reasoned, source-cited explanations. The output quality will be judged on precision, recall, and logical coherence.
Datasets
Public datasets, plus the private datasets you have access to.
Learning goals
Design and implement a graph-based agent workflow using LangGraph for legal reasoning.
Build custom tools for interacting with legal databases and compliance APIs.
Implement extended thinking patterns with adaptive reasoning budgets for complex legal problem-solving.
Integrate GPT-5 Pro for advanced legal text understanding and inference.
Develop a robust RAG system for contextual legal document retrieval.
Resources
Sample data for 2 tasks
Challenge setup
The system will be evaluated on its ability to accurately answer complex legal queries, identify compliance risks in given scenarios, and provide well-reasoned, source-cited explanations. The output quality will be ju...
Challenge input
{'query': 'string', 'contextual_documents': ['string']}
Agent execution
No agent tool is listed for this challenge.
Evaluated output
{'answer': 'string', 'citations': ['string'], 'reasoning_steps': ['string']}
- Does the answer contain factual legal errors or incorrect interpretations?
- Are all aspects of the query or scenario addressed comprehensively?
- Are at least 2 relevant citations provided for legal queries?
- AccuracyScore target: 0.9
- Python execution harness
Shows the first task, up to three tools, and up to three evidence items.
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- Does the answer contain factual legal errors or incorrect interpretations?
- Are all aspects of the query or scenario addressed comprehensively?
- Are at least 2 relevant citations provided for legal queries?
Metrics
- AccuracyScore · Target 0.9
- CoherenceScore · Target 4
Workflow tasks
- Task 1
Input: {'query': 'string', 'contextual_documents': ['string']}
Output: {'answer': 'string', 'citations': ['string'], 'reasoning_steps': ['string']}
Execution harness: Python
- Task 2
Input: {'scenario': 'string', 'regulations': ['string']}
Output: {'compliance_risks': ['string'], 'mitigation_suggestions': ['string']}
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.