Prompt2DAG: A Modular Methodology for LLM-Based Data Enrichment Pipeline Generation

• URL: http://arxiv.org/abs/2509.13487v1
• Date: Tue, 16 Sep 2025 19:40:21 GMT
• Title: Prompt2DAG: A Modular Methodology for LLM-Based Data Enrichment Pipeline Generation
• Abstract summary: We present Prompt2DAG, a methodology that transforms natural language descriptions into executable Apache Airflow DAGs.<n>We evaluate four generation approaches -- Direct, LLM-only, Hybrid, and template-based -- across 260 experiments.
• Score: 1.8696215611965206
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Developing reliable data enrichment pipelines demands significant engineering expertise. We present Prompt2DAG, a methodology that transforms natural language descriptions into executable Apache Airflow DAGs. We evaluate four generation approaches -- Direct, LLM-only, Hybrid, and Template-based -- across 260 experiments using thirteen LLMs and five case studies to identify optimal strategies for production-grade automation. Performance is measured using a penalized scoring framework that combines reliability with code quality (SAT), structural integrity (DST), and executability (PCT). The Hybrid approach emerges as the optimal generative method, achieving a 78.5% success rate with robust quality scores (SAT: 6.79, DST: 7.67, PCT: 7.76). This significantly outperforms the LLM-only (66.2% success) and Direct (29.2% success) methods. Our findings show that reliability, not intrinsic code quality, is the primary differentiator. Cost-effectiveness analysis reveals the Hybrid method is over twice as efficient as Direct prompting per successful DAG. We conclude that a structured, hybrid approach is essential for balancing flexibility and reliability in automated workflow generation, offering a viable path to democratize data pipeline development.

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