# Genicious: Contextual Few-shot Prompting for Insights Discovery

arXiv: https://arxiv.org/abs/2503.12062 · HTML: https://mlsanity.com/paper/2503.12062/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2025.

## Built on by (most cited first, 1 of 1)

- [How Much is Too Much? Exploring LoRA Rank Trade-offs for Retaining Knowledge and Domain Robustness](https://mlsanity.com/paper/2512.15634/) (2025)

## Abstract (arXiv)

Data and insights discovery is critical for decision-making in modern organizations. We present Genicious, an LLM-aided interface that enables users to interact with tabular datasets and ask complex queries in natural language. By benchmarking various prompting strategies and language models, we have developed an end-to-end tool that leverages contextual few-shot prompting, achieving superior performance in terms of latency, accuracy, and scalability. Genicious empowers stakeholders to explore, analyze and visualize their datasets efficiently while ensuring data security through role-based access control and a Text-to-SQL approach.