Evaluating Retrieval-Augmented Generation Variants for Natural Language-Based SQL and API Call Generation

Abstract: Enterprise systems increasingly require natural language interfaces that can translate user requests into structured operations such as SQL queries and REST API calls. While LLMs show promise for code generation [Chen et al., 2021; Huynh and Lin, 2025], their effectiveness in domain-specific enterprise contexts remains underexplored, particularly when both retrieval and modification tasks must be handled jointly. This paper presents a comprehensive evaluation of three retrieval-augmented generation (RAG) variants [Lewis et al., 2021] -- standard RAG, Self-RAG [Asai et al., 2024], and CoRAG [Wang et al., 2025] -- across SQL query generation, REST API call generation, and a combined task requiring dynamic task classification. Using SAP Transactional Banking as a realistic enterprise use case, we construct a novel test dataset covering both modalities and evaluate 18 experimental configurations under database-only, API-only, and hybrid documentation contexts. Results demonstrate that RAG is essential: Without retrieval, exact match accuracy is 0% across all tasks, whereas retrieval yields substantial gains in execution accuracy (up to 79.30%) and component match accuracy (up to 78.86%). Critically, CoRAG proves most robust in hybrid documentation settings, achieving statistically significant improvements in the combined task (10.29% exact match vs. 7.45% for standard RAG), driven primarily by superior SQL generation performance (15.32% vs. 11.56%). Our findings establish retrieval-policy design as a key determinant of production-grade natural language interfaces, showing that iterative query decomposition outperforms both top-k retrieval and binary relevance filtering under documentation heterogeneity.