An Agentic Retrieval Framework for Autonomous Context-Aware Data Quality Assessment
📰 ArXiv cs.AI
Learn how to implement an agentic retrieval framework for autonomous context-aware data quality assessment using AI and large language models
Action Steps
- Build an agentic retrieval framework using large language models to assess data quality in context-dependent scenarios
- Configure the framework to adapt to diverse usage scenarios and automate data quality assessment at scale
- Apply the framework to real-world data analytics pipelines to evaluate its effectiveness
- Test the framework's performance using metrics such as accuracy and efficiency
- Compare the results with existing static rule-based approaches to assess the benefits of the agentic retrieval framework
Who Needs to Know This
Data scientists and engineers working on data quality assessment and autonomous systems can benefit from this framework to improve the accuracy and efficiency of their data analytics pipelines
Key Insight
💡 An agentic retrieval framework can be used to automate data quality assessment in a context-dependent manner, improving the accuracy and efficiency of data analytics pipelines
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🚀 Autonomous context-aware data quality assessment using AI and large language models! 🤖
Key Takeaways
Learn how to implement an agentic retrieval framework for autonomous context-aware data quality assessment using AI and large language models
Full Article
Title: An Agentic Retrieval Framework for Autonomous Context-Aware Data Quality Assessment
Abstract:
arXiv:2606.13692v1 Announce Type: cross Abstract: Data quality assessment is a critical prerequisite for effective data analytics and data-driven decision-making, yet it remains a challenging task due to the inherently context-dependent nature of data quality. Existing approaches often rely on static rules or manual assessment strategies, limiting their adaptability to diverse usage scenarios and constraining automation at scale. Recent advances in artificial intelligence, particularly large lan
Abstract:
arXiv:2606.13692v1 Announce Type: cross Abstract: Data quality assessment is a critical prerequisite for effective data analytics and data-driven decision-making, yet it remains a challenging task due to the inherently context-dependent nature of data quality. Existing approaches often rely on static rules or manual assessment strategies, limiting their adaptability to diverse usage scenarios and constraining automation at scale. Recent advances in artificial intelligence, particularly large lan
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