arXiv subjects

Quentin Romero Lauro

Publications and source records attributed to Quentin Romero Lauro.

4 recordsLinked to original sources

arXiv2026

Towards Designing for Resilience: Community-Centered Deployment of an AI Business Planning Tool in a Small Business Center

Entrepreneurs in resource-constrained communities often lack time and support to translate ideas into actionable business plans. While generative AI promises assistance, most systems assume high digital literacy and overlook community infrastructures that shape adoption. We report on the community-centered design and deployment of BizChat, an AI-powered business planning tool, introduced across four workshops at a feminist makerspace in Pittsburgh. Through log data (N=30) and interviews (N=10), we examine how entrepreneurs build resilience through collective AI literacy development-encompassing adoption, adaptation, and refusal of AI. Our findings reveal that while BizChat lowered barriers to accessing capital by translating ideas into "business language," this ease raised questions about whether instant AI outputs undermine sensemaking essential to planning. We show how peer support helped entrepreneurs navigate this tension. We contribute design implications, including productive friction, communal scaffolds, and co-optability, for strengthening resilience amid technological change.

cs.HC↗

arXiv2025

BizChat: Scaffolding AI-Powered Business Planning for Small Business Owners Across Digital Skill Levels

Generative AI can help small business owners automate tasks, increase efficiency, and improve their bottom line. However, despite the seemingly intuitive design of systems like ChatGPT, significant barriers remain for those less comfortable with technology. To address these disparities, prior work highlights accessory skills -- beyond prompt engineering -- users must master to successfully adopt generative AI including keyboard shortcuts, editing skills, file conversions, and browser literacy. Building on a design workshop series and 15 interviews with small businesses, we introduce BizChat, a large language model (LLM)-powered web application that helps business owners across digital skills levels write their business plan -- an essential but often neglected document. To do so, BizChat's interface embodies three design considerations inspired by learning sciences: ensuring accessibility to users with less digital skills while maintaining extensibility to power users ("low-floor-high-ceiling"), providing in situ micro-learning to support entrepreneurial education ("just-in-time learning"), and framing interaction around business activities ("contextualized technology introduction"). We conclude with plans for a future BizChat deployment.

cs.HC↗

arXiv2025

RAG Without the Lag: Interactive Debugging for Retrieval-Augmented Generation Pipelines

Retrieval-augmented generation (RAG) pipelines have become the de-facto approach for building AI assistants with access to external, domain-specific knowledge. Given a user query, RAG pipelines typically first retrieve (R) relevant information from external sources, before invoking a Large Language Model (LLM), augmented (A) with this information, to generate (G) responses. Modern RAG pipelines frequently chain multiple retrieval and generation components, in any order. However, developing effective RAG pipelines is challenging because retrieval and generation components are intertwined, making it hard to identify which component(s) cause errors in the eventual output. The parameters with the greatest impact on output quality often require hours of pre-processing after each change, creating prohibitively slow feedback cycles. To address these challenges, we present RAGGY, a developer tool that combines a Python library of composable RAG primitives with an interactive interface for real-time debugging. We contribute the design and implementation of RAGGY, insights into expert debugging patterns through a qualitative study with 12 engineers, and design implications for future RAG tools that better align with developers' natural workflows.

cs.HC↗

arXiv2024

Exploring the Role of Social Support when Integrating Generative AI into Small Business Workflows

Small business owners stand to benefit from generative AI technologies due to limited resources, yet they must navigate increasing legal and ethical risks. In this paper, we interview 11 entrepreneurs and support personnel to investigate existing practices of how entrepreneurs integrate generative AI technologies into their business workflows. Specifically, we build on scholarship in HCI which emphasizes the role of small, offline networks in supporting entrepreneurs' technology maintenance. We detail how entrepreneurs resourcefully leveraged their local networks to discover new use cases of generative AI (e.g., by sharing accounts), assuage heightened techno-anxieties (e.g., by recruiting trusted confidants), overcome barriers to sustained use (e.g., by receiving wrap-around support), and establish boundaries of use. Further, we suggest how generative AI platforms may be redesigned to better support entrepreneurs, such as by taking into account the benefits and tensions of use in a social context.

cs.HC↗