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RAG strategies boost SQL and API call generation from natural language

By PulseAugur Editorial · [1 sources] ·

A new research paper explores strategies for retrieval-augmented generation (RAG) to improve natural language interfaces for enterprise software. The study focuses on generating SQL queries and REST API calls, tasks that typically require specialized technical knowledge. By evaluating standard RAG, Self-RAG, and CoRAG on a dataset derived from SAP Transactional Banking, the research found that retrieval augmentation is crucial for reliable structured generation. The findings indicate that CoRAG performed best in a combined SQL and REST API generation scenario, particularly under mixed-documentation conditions. AI

IMPACT Enhances natural language interfaces for enterprise software, potentially simplifying data access and API interactions.

RANK_REASON Research paper published on arXiv detailing RAG strategies for structured data generation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

RAG strategies boost SQL and API call generation from natural language

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Research paper published on arXiv detailing RAG strategies for structured data generation. [lever_c_demoted from research: ic=1 ai=1.0]
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  1. arXiv cs.CL TIER_1 English(EN) · Tim Schlippe, Simon Martin, Michael Marketsm\"uller ·

    RAG Strategies for Natural Language-Based SQL Query and REST API Call Generation

    arXiv:2602.07086v2 Announce Type: replace-cross Abstract: Enterprise software systems commonly expose business functionality through both relational databases and REST APIs. Accessing these interfaces requires specialized technical knowledge, as users must determine whether a req…

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