arXiv:2605.25297v1 Announce Type: new Abstract: Effective features are crucial for predictive model performance, but creating them often requires domain expertise, limiting scalability across applications. We define feature engineering as an agentic code generation problem: features are not static data transformations, but executable programs that can be generated, evaluated, and iteratively improved. We present Eureka, an LLM-driven framework with three stages. (1) An Expert Agent, fine-tuned via SFT on domain knowledge, produces structured feature design plans in JSON format. (2) An LLM Feat
The increasing complexity of AI models and the critical need for robust predictive performance are driving innovation in automated feature engineering, especially for enterprise applications.
Automating feature engineering with LLMs can significantly reduce the technical expertise required to deploy high-performing AI systems, accelerating adoption and effectiveness across various industries.
The process of developing and fine-tuning predictive models can become more efficient and scalable, potentially lowering barriers to entry for AI solution development and deployment.
- · Enterprise AI platform providers
- · Data scientists
- · Cloud resource providers
- · Businesses leveraging predictive analytics
- · Manual feature engineering consultancies
- · Companies slow to adopt automated AI tools
Widespread adoption of LLM-driven feature engineering frameworks for enterprise AI applications.
Increased efficiency and accuracy in cloud resource allocation and demand forecasting, leading to cost savings and improved service reliability.
The development of more sophisticated and autonomous AI agents capable of end-to-end model development and deployment with minimal human intervention.
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