mlsanityData to 7 Oct 2026

Building Your Own Product Copilot: Challenges, Opportunities, and Needs

arXiv 2312.14231 ↗First posted 21 Dec 2023cs.SEcited by 17

Not on the map. Why?

Abstract (arXiv)

A race is underway to embed advanced AI capabilities into products. These product copilots enable users to ask questions in natural language and receive relevant responses that are specific to the user's context. In fact, virtually every large technology company is looking to add these capabilities to their software products. However, for most software engineers, this is often their first encounter with integrating AI-powered technology. Furthermore, software engineering processes and tools have not caught up with the challenges and scale involved with building AI-powered applications. In this work, we present the findings of an interview study with 26 professional software engineers responsible for building product copilots at various companies. From our interviews, we found pain points at every step of the engineering process and the challenges that strained existing development practices. We then conducted group brainstorming sessions to collaborative on opportunities and tool designs for the broader software engineering community.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Dec 2023.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 9; 2025: 2; 2026: 6.

17 papers.

Sort:

By area:

  1. 2024MetaReflection: Learning Instructions for Language Agents using Past Reflectionscited by 19
  2. 2024Prompts Are Programs Too! Understanding How Developers Build Software Containing Promptscited by 19
  3. 2024Large Language Models as Software Components: A Taxonomy for LLM-Integrated Applicationscited by 17
  4. 2024Beyond the Comfort Zone: Emerging Solutions to Overcome Challenges in Integrating LLMs into Software Productscited by 11
  5. 2024Rethinking Software Engineering in the Foundation Model Era: A Curated Catalogue of Challenges in the Development of Trustworthy FMwarecited by 6
  6. 2024The Evolution of LLM Adoption in Industry Data Curation Practicescited by 6
  7. 2024Design and evaluation of AI copilots -- case studies of retail copilot templatescited by 3
  8. 2024Meaning Typed Prompting: A Technique for Efficient, Reliable Structured Output Generationcited by 2
  9. 2024From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmapcited by 2
  10. 2026Agentic Pipelines in Embedded Software Engineering: Emerging Practices and Challengescited by 2
  11. 2025Understanding Prompt Programming Tasks and Questionscited by 1
  12. 2026How Do Practitioners Build SE Agents? Insights from a Mixed-Methods Studycited by 1
5 more
  1. 2026Agent-Integrated Software: Interaction Contracts and Continuous Assurancecited by 1
  2. 2025The Hitchhikers Guide to Production-ready Trustworthy Foundation Model powered Software (FMware)cited by 0
  3. 2026Results-Actionability Gap: Understanding How Practitioners Evaluate LLM Products in the Wildcited by 0
  4. 2026The Agentic Company OS: Substrate Inversion for Sustained Enterprise Agent Deploymentcited by 0
  5. 2026Developing a Roadmap to an AI-first Organization: A Case Study in Embedded Software Developmentcited by 0