Data Analytics with Large Language Models (LLM): A Novel Prompting Framework

Shamma Mubarak Aylan Abdulla Almheiri (), Mohammad AlAnsari (), Jaber AlHashmi (), Noha Abdalmajeed (), Muhammed Jalil () and Gurdal Ertek ()
Additional contact information
Shamma Mubarak Aylan Abdulla Almheiri: United Arab Emirates University
Mohammad AlAnsari: United Arab Emirates University
Jaber AlHashmi: United Arab Emirates University
Noha Abdalmajeed: United Arab Emirates University
Muhammed Jalil: United Arab Emirates University
Gurdal Ertek: United Arab Emirates University

Chapter Chapter 20 in Business Analytics and Decision Making in Practice, 2024, pp 243-255 from Springer

Abstract: Abstract This study presents a novel framework for conducting data analytics using Large Language Models (LLMs). The proposed framework suggests the construction of prompts and interaction patterns using four fundamental constructs: meta-specifications, specifications, instructions, and prompting patterns. The framework can guide and assist data engineers, analysts, and even non-technical domain experts by providing these four constructs as palettes of options. The LLM can then suggest analytics designs, conduct the analysis, provide posterior interpretations and insights, and produce other outputs, such as code or packaged software. The presented novel framework covers an immense space of possibilities through numerous combinations of selected meta-specifications, specifications, instructions, and prompting patterns. The primary theoretical contribution of this research is that it proposes a theoretical foundation and frame of reference for conducting data analytics using LLM. The primary practical contribution is that LLMs can now be employed much more systematically and extensively than before in designing and conducting data analytics. This opens a new world of applications powered by a countless combination of the four constructs across practically all fields of science, technology, and business, where LLMs can be used to guide, conduct, and interpret the results of data analytics.

Keywords: Data analytics; Large language models; ChatGPT; Theoretical framework; Bottom-up conceptual analysis (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-3-031-61589-4_20

Ordering information: This item can be ordered from
http://www.springer.com/9783031615894

DOI: 10.1007/978-3-031-61589-4_20

Access Statistics for this chapter

More chapters in Lecture Notes in Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().