A systems engineer argues that the problem of persisting corrections made by experts to large language model (LLM) errors is an operations issue, not a tooling problem. The author maps the LLM stack onto existing systems engineering concepts and identifies where the mapping fails. A seven-principle operating discipline with an error loop at its core is proposed, along with three case studies from the author's own practice. The article suggests that this approach could improve the reliability of LLMs. --- Why it matters: This matters to AI engineers because it highlights a critical operational challenge in deploying large language models, and proposes a framework for addressing it. By improving the persistence and governance of corrections made by experts, AI systems can become more reliable and trustworthy. Source: https://arxiv.org/abs/2608.19125