Tuning the Stochastic Machine: A Systems Engineer's Operating Model for Human-AI Engineering
2026-08-19 • Artificial Intelligence
Artificial IntelligenceSoftware Engineering
AI summaryⓘ
The authors explain that when experts fix mistakes made by language AI assistants, the fixes often don’t last beyond the current session, causing the same errors to return later. They argue this is due to how these corrections are managed, not a lack of tools, since systems to save fixes exist but lack proper rules and processes to maintain them effectively. By comparing AI systems to traditional computing layers, the authors find gaps like unpredictable outputs and missing verification steps. They propose seven guiding rules to better manage error corrections and share real-world examples demonstrating their approach. Finally, they suggest ways to measure and study these improvements.
large language modelserror correctionpersistenceprovenancerecurrence monitoringverificationoperating disciplinestochastic generationconfiguration management
Authors
George Andrikopoulos
Abstract
When an expert corrects an LLM assistant's error, the correction usually dies with the session, and the error class returns. I argue this is an operations problem, not a tooling problem: mechanisms for persisting corrections exist and are shipping, but the discipline for governing them -- versioning with provenance, recurrence monitoring, counter-metrics, retirement of stale rules -- does not. Writing as a systems engineer of thirty years, I map the LLM stack onto the machines my profession already operates (frozen silicon, firmware, loadable modules, persistent configuration, volatile memory), identify where the mapping fails (stochastic generation, configuration that binds only probabilistically, no general-purpose retirement (verification) stage by default), and derive from the failures a seven-principle operating discipline with an error loop at its core. Three cases from my own practice illustrate the mechanism, among them a control that silently became the exact harm it was built to prevent. I close with the measurement framework this view implies and the lab study required to test it.