# Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead

arXiv: https://arxiv.org/abs/2506.23762 · HTML: https://mlsanity.com/paper/2506.23762/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2025.

## Built on by (most cited first, 2 of 2)

- [Foundational Design Principles and Patterns for Building Robust and Adaptive GenAI-Native Systems](https://mlsanity.com/paper/2508.15411/) (2025)
- [LaQual: An Automated Framework for LLM App Quality Evaluation](https://mlsanity.com/paper/2508.18636/) (2025)

## Abstract (arXiv)

The rapid advancement of large language models (LLMs) has redefined artificial intelligence (AI), pushing the boundaries of AI research and enabling unbounded possibilities for both academia and the industry. However, LLM development faces increasingly complex challenges throughout its lifecycle, yet no existing research systematically explores these challenges and solutions from the perspective of software engineering (SE) approaches. To fill the gap, we systematically analyze research status throughout the LLM development lifecycle, divided into six phases: requirements engineering, dataset construction, model development and enhancement, testing and evaluation, deployment and operations, and maintenance and evolution. We then conclude by identifying the key challenges for each phase and presenting potential research directions to address these challenges. In general, we provide valuable insights from an SE perspective to facilitate future advances in LLM development.