Jul 2026· Global Journal of Engineering and Technology Advances· 0 citations
Abstract
In today’s data-driven environment, the ability to explore and manipulate datasets is essential, however, many existing data analysis tools require technical expertise in platforms such as Excel, Python, or Power BI, creating accessibility barriers for non-technical users. This project presents Quiksight, a web-based conversational data analysis system powered by Large Language Models (LLMs) that enables users to interact with datasets using natural language. The objective of this work was to develop an intuitive system that simplifies data analysis by allowing users to upload Excel or CSV files and perform operations such as filtering, sorting, summarization, column modification, missing-value handling, and data export without writing code. The system was implemented using Google Gemini as the LLM, FastAPI for backend processing, and HTML, JavaScript, and Tailwind CSS for the frontend interface.Evaluation showed that Quiksight successfully interpreted and executed a wide range of natural language data manipulation queries with high accuracy. Testing recorded an average query response time of approximately 2 seconds, while dataset uploads up to 30MB remained below 20 seconds under suitable network conditions. Compared with existing platforms such as Julius.ai and PowerDrill.ai, Quiksight demonstrated advantages through its lightweight architecture, simplified interaction model, and focus on everyday users. The project demonstrates the feasibility of LLM-powered conversational interfaces for democratizing data analysis. Future improvements include database integration, support for larger datasets, and advanced analytical capabilities such as statistical modelling.
The resulting prototype confirms that a cloud-hosted multimodal LLM, when combined with a minimal and well-structured web stack, can serve as a practical foundation for next-generation digital assistants suitable for customer support, education, and personal productivity applications.
G. Vamsi, Vinay Kumar Male· International Scientific Jou...· 0 citations
This paper explores the practical implementation of a Chatbot designed to connect natural language communication with database interactions by generating Structured Query Language (SQL) queries. Utilizing the OpenAPI framework, a widely-used specification for building APIs, our approach aims to enhance the flexibility and interoperability of the ChatBot. The system not only interprets user queries expressed in natural language but also transforms them into syntactically correct and semantically meaningful SQL commands. This paper provides an overview of the current landscape of natural language processing (NLP) Chatbots, discusses the architecture and design considerations of our implementation, and highlights the methodology for training and fine-tuning using state-of-the-art NLP models. Real-world use cases are presented to showcase the practical applicability of the Chatbot, demonstrating its ability to handle various complexities inherent in natural language queries. This work contributes to the field by presenting a scalable and robust solution that combines NLP and database interactions through the utilization of OpenAPI, with potential implications for improving human-computer interactions within database systems.
R. Santosa, Hartantya Ainiyatuts Tsaniyah, Yoga Ari Tofan et al.· IPTEK: The Journal for Techn...· 0 citations
How users actually use LLM-powered data systems is examined: how they struggle to specify and validate queries, how they iterate extensively to converge on bespoke operators specific to their documents and domains, and what these patterns imply for the systems and benchmarks their community should be building.
Shreya Shankar, Aditya G. Parameswaran, U. Berkeley· 0 citations
BACKGROUND
Large language models have accelerated the adoption of generative artificial intelligence (AI), making AI tools more widely accessible through conversational prompting. One emerging application is vibe coding, in which users use natural-language prompts to generate code and desired outputs rather than manually writing traditional code.
OBJECTIVE
To examine AI-assisted, human-in-the-loop (HITL) vibe coding as a proof of concept for data analysis, describe its components and a proposed workflow with explicit safeguards, and present a case study illustrating its use and potential failure points.
METHODS
We used a proposed workflow that included framing research questions, operationalizing variables, organizing project folders, documenting decisions, applying retrieval-augmented generation, and using prompt engineering techniques. We used Cursor (v1.5.11) on a limited, clean admissions data set, in which admission status was modeled as a function of the Graduate Record Exam, grade point average, and undergraduate rank. Logistic regression was generated via conversational prompts, implemented in R, and the results were compared with a published reference output on a publicly available website.
RESULTS
AI-assisted, HITL vibe coding produced statistical codes that included schema checks, range validations, data cleaning, exploratory analyses, regression modeling, and visualization. There were mixed results of both valid and invalid outputs. Regression coefficients, p values, and model fit statistics matched the outputs posted on the published reference output website. However, an error was identified in the predicted-probability confidence interval output, which was missed during the initial review of outputs.
DISCUSSION
While vibe coding has the potential to reduce barriers to data analysis for researchers, this case study demonstrated that it can produce both valid and invalid outputs and that foundational statistical training, knowledge, understanding, and methodological expertise remain paramount when using it. Future studies should address important empirical questions about the use of vibe coding, such as under what conditions it can be safely used in research and what kinds of errors are most commonly generated when using it. AI-assisted HITL vibe coding should be used with caution and only with structured verification and safeguards, transparent reporting, and appropriate statistical and methodological oversight.
D. Tolentino, E. Kohout, Paul Boy et al.· Nursing Research· 0 citations
The design realization and evaluation of an Automated Summarization Tool (AST) is presented which is a document intelligence platform based on google gemini 2.5 flash that outperforms the strongest fine-tuned transformer baselines (PEGASUS, BART) by ~14 points and is clearly ahead of BERTSUM-ext (a strong transformer baseline), Pointer-Generator Network, TextRank.
K. Kumar, A. Amandeep, Dharmender Kumar et al.· International Journal of Inn...· 0 citations
This paper discusses the design, implementation, and evaluation of a conversational interface that provides users with real-time, context-aware responses to queries, and demonstrates how such AI-powered systems enhance user experience, improve query accuracy, and streamline data discovery processes.
Steven Young, Rebecca Green· International Journal of Dat...· 0 citations