# Controlling Context: Generative AI at Work in Integrated Circuit Design and Other High-Precision Domains

arXiv: https://arxiv.org/abs/2506.14567 · HTML: https://mlsanity.com/paper/2506.14567/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 34 of its 119 references that are in the map.
- First posted on arXiv 17 Jun 2025.

## Builds on (most cited first, 30 of 34)

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- [SPADE: Synthesizing Data Quality Assertions for Large Language Model Pipelines](https://mlsanity.com/paper/2401.03038/) (2024)
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- [The Role of Generative AI in Software Development Productivity: A Pilot Case Study](https://mlsanity.com/paper/2406.00560/) (2024)
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## Abstract (arXiv)

Generative AI tools have become more prevalent in engineering workflows, particularly through chatbots and code assistants. As the perceived accuracy of these tools improves, questions arise about whether and how those who work in high-precision domains might maintain vigilance for errors, and what other aspects of using such tools might trouble their work. This paper analyzes interviews with hardware and software engineers, and their collaborators, who work in integrated circuit design to identify the role accuracy plays in their use of generative AI tools and what other forms of trouble they face in using such tools. The paper inventories these forms of trouble, which are then mapped to elements of generative AI systems, to conclude that controlling the context of interactions between engineers and the generative AI tools is one of the largest challenges they face. The paper concludes with recommendations for mitigating this form of trouble by increasing the ability to control context interactively.