Retrieval-Augmented Analytics Assistants for Self-Serve Insights in SQL and Python

Registration ID: IJNRD_311684

Published ID: IJNRD2601109

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Yashika Shankheshwaria

retrieval-augmented generation, large language models, SQL reasoning, text-to-SQL, semantic SQL, intelligent agents, big data analytics, self-serve BI

Organizations generate large volumes of data, yet the ability of non-technical stakeholders to extract insights from the data is limited. Traditional analytics workflows rely on the analysts who help translate business questions into SQL queries. They are able to translate the questions into Python code or dashboards. Important to note that this independence limits business growth as it bottlenecks decision- making, and it also restricts the democratization of the data insights. Recent breakthroughs in retrieval- augmented generation (RAG), intelligent agentic systems, and large language models (LLMs) have opened new pathways for self-serve analytics. Important to note that Retrieval-augmented analytics combine the natural-language interfaces with semantic SQL reasoning, a retrieval mechanism, and automated SQL or Python generation to enable users to query organizational data without manual coding. The paper examines the capabilities, design, and implications of RAG-enhanced analytics assistants. The paper draws insights from scholarly sources. Using a qualitative synthesis and design-science methodology, the paper will integrate insights from the studies of develop a reference architecture and design principles to support retrieval-augmented assistants that are capable of trustworthy self-serve analytics. The findings depict that although RAG- enabled SQL/Python assistants offer benefits like improved accessibility, dynamic retrieval of knowledge, and automation of query reasoning. There are many challenges that remain, and they include semantic ambiguity, hallucinations, and the need for governance, robust semantic layers, and evaluation benchmarks. This research paper proposes a theoretical and practical framework that can be used to deploy retrieval-augmented analytics assistants in real-world environments.

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Yashika Shankheshwaria (January-2026). Retrieval-Augmented Analytics Assistants for Self-Serve Insights in SQL and Python. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(1), b62-b66. https://ijnrd.org/papers/IJNRD2601109.pdf

Volume 11 Issue 1, January-2026

Pages : b62-b66

Paper Reg. ID: IJNRD_311684

Published Paper Id: IJNRD2601109

Research Area: Other area not in list

Author Type: Foreign Author

Country: -, -, United States

Published Paper PDF: https://ijnrd.org/papers/IJNRD2601109.pdf

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