Tuning the Stochastic Machine: A Systems Engineer's Operating Model for Human-AI Engineering

The paper argues that when a human corrects an LLM assistant’s mistake, the correction often disappears after the session ends, highlighting an operations issue rather than a tooling one. Drawing on thirty years of systems engineering experience, the author maps the LLM stack onto traditional hardware and software components, identifies mismatches—such as stochastic generation and lack of a retirement stage—and proposes a seven‑principle operating discipline centered on an error loop. The paper includes three real‑world cases, one of which illustrates how a control mechanism can inadvertently cause the harm it was meant to prevent, and concludes with a suggested measurement framework and a lab study to validate the approach.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at Hugging Face Trending Papers.

• llms