Prompt Engineering: A Framework for Automated SQL Generation, Schema Validation, and Anomaly Detection Using Large Language Models

Sougandhika Tera

doi:10.71097/IJAIDR.v16.i2.1663

Prompt Engineering: A Framework for Automated SQL Generation, Schema Validation, and Anomaly Detection Using Large Language Models

Author(s)	Sougandhika Tera
Country	United States
Abstract	The convergence of Large Language Models (LLMs) and data engineering ushers in a new era of cognitive automation, which can greatly improve data pipeline reliability, efficiency, and governance. This paper provides a comprehensive framework for using structured prompt engineering to perform three critical data engineering functions: (1) context-aware SQL query generation and optimization, (2) automated schema compatibility validation and drift detection, and (3) proactive data anomaly detection using statistical and semantic analysis. We offer novel prompt design patterns, such as Meta-Context Retrieval, Multi-Agent Validation Chains, and Feedback-Aware Prompt Tuning that use dynamic metadata from data catalogs to generate correct, production-ready results. Furthermore, we propose a layered safety architecture that includes Prompt Sanitization, Semantic Guardrails, and Human-in-the-Loop (HITL) checkpoints to reduce the risks associated with LLM hallucinations and logical errors. Empirical discussion, supported by current literature and real scenarios, shows that systematic rapid engineering can cut development time by up to 60% for common activities while enhancing data quality adherence. We conclude that prompt engineering is evolving from an auxiliary skill into a core data-engineering competency, essential for building resilient, self-documenting, and intelligent data systems in the AI-augmented era.
Keywords	Prompt engineering, data engineering, large language models (LLMs), SQL generation, schema validation, anomaly detection, data quality, metadata context, AI governance, data pipelines, automated ETL
Field	Engineering
Published In	Volume 16, Issue 2, July-December 2025
Published On	2025-12-20
Cite This	Prompt Engineering: A Framework for Automated SQL Generation, Schema Validation, and Anomaly Detection Using Large Language Models - Sougandhika Tera - IJAIDR Volume 16, Issue 2, July-December 2025. DOI 10.71097/IJAIDR.v16.i2.1663
DOI	https://doi.org/10.71097/IJAIDR.v16.i2.1663
Short DOI	https://doi.org/hbkqrm

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Prompt Engineering: A Framework for Automated SQL Generation, Schema Validation, and Anomaly Detection Using Large Language Models

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