# The Systems Engineering Approach in Times of Large Language Models

arXiv: https://arxiv.org/abs/2411.09050 · HTML: https://mlsanity.com/paper/2411.09050/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 44 references that are in the map.
- First posted on arXiv 13 Nov 2024.

## Builds on (most cited first, 12 of 12)

- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [Explainability for Large Language Models: A Survey](https://mlsanity.com/paper/2309.01029/) (2023)
- [Auditing large language models: a three-layered approach](https://mlsanity.com/paper/2302.08500/) (2023)
- [Generative AI](https://mlsanity.com/paper/2309.07930/) (2023)
- [Technology Readiness Levels for AI & ML](https://mlsanity.com/paper/2006.12497/) (2020)
- [Generative AI and Process Systems Engineering: The Next Frontier](https://mlsanity.com/paper/2402.10977/) (2024)
- [Requirements are All You Need: The Final Frontier for End-User Software Engineering](https://mlsanity.com/paper/2405.13708/) (2024)
- [Core and Periphery as Closed-System Precepts for Engineering General Intelligence](https://mlsanity.com/paper/2208.02837/) (2022)
- [Machine Learning Systems: A Survey from a Data-Oriented Perspective](https://mlsanity.com/paper/2302.04810/) (2023)
- [Five Ps: Leverage Zones Towards Responsible AI](https://mlsanity.com/paper/2205.01070/) (2022)
- [Self-sustaining Software Systems (S4): Towards Improved Interpretability and Adaptation](https://mlsanity.com/paper/2401.11370/) (2024)
- [Understanding and Modeling AI-Intensive System Development](https://mlsanity.com/paper/2103.09191/) (2021)

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

- [LLM Performance for Code Generation on Noisy Tasks](https://mlsanity.com/paper/2505.23598/) (2025)

## Abstract (arXiv)

Using Large Language Models (LLMs) to address critical societal problems requires adopting this novel technology into socio-technical systems. However, the complexity of such systems and the nature of LLMs challenge such a vision. It is unlikely that the solution to such challenges will come from the Artificial Intelligence (AI) community itself. Instead, the Systems Engineering approach is better equipped to facilitate the adoption of LLMs by prioritising the problems and their context before any other aspects. This paper introduces the challenges LLMs generate and surveys systems research efforts for engineering AI-based systems. We reveal how the systems engineering principles have supported addressing similar issues to the ones LLMs pose and discuss our findings to provide future directions for adopting LLMs.