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

arXiv: https://arxiv.org/abs/2312.14231 · HTML: https://mlsanity.com/paper/2312.14231/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2023.

## Built on by (most cited first, 17 of 17)

- [MetaReflection: Learning Instructions for Language Agents using Past Reflections](https://mlsanity.com/paper/2405.13009/) (2024)
- [Prompts Are Programs Too! Understanding How Developers Build Software Containing Prompts](https://mlsanity.com/paper/2409.12447/) (2024)
- [Large Language Models as Software Components: A Taxonomy for LLM-Integrated Applications](https://mlsanity.com/paper/2406.10300/) (2024)
- [Beyond the Comfort Zone: Emerging Solutions to Overcome Challenges in Integrating LLMs into Software Products](https://mlsanity.com/paper/2410.12071/) (2024)
- [Rethinking Software Engineering in the Foundation Model Era: A Curated Catalogue of Challenges in the Development of Trustworthy FMware](https://mlsanity.com/paper/2402.15943/) (2024)
- [The Evolution of LLM Adoption in Industry Data Curation Practices](https://mlsanity.com/paper/2412.16089/) (2024)
- [Design and evaluation of AI copilots -- case studies of retail copilot templates](https://mlsanity.com/paper/2407.09512/) (2024)
- [Meaning Typed Prompting: A Technique for Efficient, Reliable Structured Output Generation](https://mlsanity.com/paper/2410.18146/) (2024)
- [From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmap](https://mlsanity.com/paper/2410.20791/) (2024)
- [Agentic Pipelines in Embedded Software Engineering: Emerging Practices and Challenges](https://mlsanity.com/paper/2601.10220/) (2026)
- [How Do Practitioners Build SE Agents? Insights from a Mixed-Methods Study](https://mlsanity.com/paper/2607.10856/) (2026)
- [Understanding Prompt Programming Tasks and Questions](https://mlsanity.com/paper/2507.17264/) (2025)
- [Agent-Integrated Software: Interaction Contracts and Continuous Assurance](https://mlsanity.com/paper/2609.11381/) (2026)
- [The Hitchhikers Guide to Production-ready Trustworthy Foundation Model powered Software (FMware)](https://mlsanity.com/paper/2505.10640/) (2025)
- [Results-Actionability Gap: Understanding How Practitioners Evaluate LLM Products in the Wild](https://mlsanity.com/paper/2604.16304/) (2026)
- [The Agentic Company OS: Substrate Inversion for Sustained Enterprise Agent Deployment](https://mlsanity.com/paper/2609.13334/) (2026)
- [Developing a Roadmap to an AI-first Organization: A Case Study in Embedded Software Development](https://mlsanity.com/paper/2609.30863/) (2026)

## 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.