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Balancing Content Size in RAG-Text2SQL System

Prakhar Gurawa, Anjali Dharmik

TL;DR

This paper investigates how document size and quality in Retrieval-Augmented Generation affect Text-to-SQL performance, using SPIDER-based experiments to quantify retrieval quality and SQL-generation fidelity. It demonstrates that richer document content can improve retrieval discrimination but often increases prompt length and hallucination risk, with a notable sweet spot around succinct, well-structured descriptions. The study proposes a framework for dynamic, context-aware document design and prompt strategies to balance RAG benefits against Text2SQL reliability, offering actionable guidelines for real-world systems. Overall, the findings illuminate the trade-offs between context richness and prompt manageability, providing practical paths to robust RAG+Text2SQL deployments in enterprise data environments.

Abstract

Large Language Models (LLMs) have emerged as a promising solution for converting natural language queries into SQL commands, enabling seamless database interaction. However, these Text-to-SQL (Text2SQL) systems face inherent limitations, hallucinations, outdated knowledge, and untraceable reasoning. To address these challenges, the integration of retrieval-augmented generation (RAG) with Text2SQL models has gained traction. RAG serves as a retrieval mechanism, providing essential contextual information, such as table schemas and metadata, to enhance the query generation process. Despite their potential, RAG + Text2SQL systems are susceptible to the quality and size of retrieved documents. While richer document content can improve schema relevance and retrieval accuracy, it also introduces noise, increasing the risk of hallucinations and reducing query fidelity as the prompt size of the Text2SQL model increases. This research investigates the nuanced trade-off between document size and quality, aiming to strike a balance that optimizes system performance. Key thresholds are identified where performance degradation occurs, along with actionable strategies to mitigate these challenges. Additionally, we explore the phenomenon of hallucinations in Text2SQL models, emphasizing the critical role of curated document presentation in minimizing errors. Our findings provide a roadmap for enhancing the robustness of RAG + Text2SQL systems, offering practical insights for real-world applications.

Balancing Content Size in RAG-Text2SQL System

TL;DR

This paper investigates how document size and quality in Retrieval-Augmented Generation affect Text-to-SQL performance, using SPIDER-based experiments to quantify retrieval quality and SQL-generation fidelity. It demonstrates that richer document content can improve retrieval discrimination but often increases prompt length and hallucination risk, with a notable sweet spot around succinct, well-structured descriptions. The study proposes a framework for dynamic, context-aware document design and prompt strategies to balance RAG benefits against Text2SQL reliability, offering actionable guidelines for real-world systems. Overall, the findings illuminate the trade-offs between context richness and prompt manageability, providing practical paths to robust RAG+Text2SQL deployments in enterprise data environments.

Abstract

Large Language Models (LLMs) have emerged as a promising solution for converting natural language queries into SQL commands, enabling seamless database interaction. However, these Text-to-SQL (Text2SQL) systems face inherent limitations, hallucinations, outdated knowledge, and untraceable reasoning. To address these challenges, the integration of retrieval-augmented generation (RAG) with Text2SQL models has gained traction. RAG serves as a retrieval mechanism, providing essential contextual information, such as table schemas and metadata, to enhance the query generation process. Despite their potential, RAG + Text2SQL systems are susceptible to the quality and size of retrieved documents. While richer document content can improve schema relevance and retrieval accuracy, it also introduces noise, increasing the risk of hallucinations and reducing query fidelity as the prompt size of the Text2SQL model increases. This research investigates the nuanced trade-off between document size and quality, aiming to strike a balance that optimizes system performance. Key thresholds are identified where performance degradation occurs, along with actionable strategies to mitigate these challenges. Additionally, we explore the phenomenon of hallucinations in Text2SQL models, emphasizing the critical role of curated document presentation in minimizing errors. Our findings provide a roadmap for enhancing the robustness of RAG + Text2SQL systems, offering practical insights for real-world applications.

Paper Structure

This paper contains 26 sections, 1 equation, 7 figures, 1 table.

Table of Contents

  1. Introduction
  2. Why This Problem Matters
  3. Objectives of the Paper
  4. Related Work
  5. Text-to-SQL Models
  6. Impact of Document Characteristics on RAG Systems
  7. Prompt Design and Its Influence on LLM Performance
  8. Hallucination in LLMs and RAG Systems
  9. Problem Definition
  10. Experimental Setup
  11. About Data Set: Spider
  12. Document Variations
  13. Document Quality vs. Size Trade-off
  14. Description of RAG system
  15. About Text2SQL Model
  16. ...and 11 more sections

Figures (7)

  • Figure 1: End-to-End Pipeline of the RAG + Text2SQL System
  • Figure 2: Variation of Avg. Range of Scores over different document sets
  • Figure 3: Variation of Avg. Std Dev of Scores over different document sets
  • Figure 4: Variation of Avg. DCG of Scores over different document sets
  • Figure 5: Percentage of queries marked as similar for each similarity metric across the different document sets.
  • ...and 2 more figures