Policy : Use Feedback Loops to Continuously Improve AI Performance

Commitment to Production Feedback Loops in AI AI systems that are disconnected from the outcomes of their own predictions cannot improve. Without feedback, a model that begins to degrade has no mechanism for self-correction. Without feedback, the cases where the model was wrong are invisible to the engineers responsible for improving it. Without feedback, retraining is informed by historical data rather than recent production experience — and recent production experience is precisely where the most valuable learning lives. Our commitment is to build feedback loops as a first-class component of every AI system — explicit, instrumented, and actively maintained.

What This Means Building feedback loops means designing AI systems with the assumption that production will produce information that needs to flow back into the model. It means creating mechanisms to capture that information — user corrections, outcome labels, implicit behavioural signals — and route it into the improvement cycle. It means investing in the labelling and annotation infrastructure needed to turn raw feedback into training-quality data. And it means closing the loop explicitly: using feedback to retrain, evaluating whether the retrained model is better, and tracking improvement over time.

Our commitment to using feedback loops to improve AI performance is built on:

• Explicit Feedback Signal Design – Every AI system has a documented feedback strategy: what signals will be captured from production, how they will be captured, how they will be validated and labelled, and how they will be incorporated into model improvement. Feedback design is part of system design, not an operational afterthought.
• User Correction Capture – Where AI systems produce outputs that users can accept, correct, or reject, those correction signals are captured and stored. User corrections are among the most valuable feedback signals available — they are direct labels on real production cases from domain experts.
• Implicit Signal Instrumentation – Beyond explicit corrections, we capture implicit signals from user behaviour: which AI recommendations were acted upon, which were ignored, where users deviated from AI suggestions. These signals provide feedback even where users do not explicitly engage with correction mechanisms.
• Outcome Tracking – For AI systems making predictions that have measurable real-world outcomes, we track whether predicted outcomes materialised. This ground-truth feedback is the most powerful signal for model improvement and is prioritised as labelled training data.
• Feedback Data Quality – Feedback data is treated with the same quality standards as original training data. It is validated, deduplicated, reviewed for labelling consistency, and maintained with full lineage. Poor-quality feedback data degrades rather than improves models.
• Active Retraining Triggers – Model retraining is triggered by feedback volume thresholds, performance drift metrics, and time-based cadences — not ad hoc when engineers find time. Retraining is a scheduled operational activity, not a reactive crisis response.
• Feedback Loop Effectiveness Measurement – We measure whether feedback loops are producing the improvement they should. If model performance is not improving despite significant feedback volume, we investigate whether the feedback is of sufficient quality, whether it is being incorporated correctly, and whether the model architecture is capable of learning the patterns the feedback reveals.

Why This Matters AI systems without feedback loops are in a race against time — they were trained on historical data, and as the world moves on, their knowledge becomes stale. Feedback loops are the mechanism that converts AI from a static approximation of a historical world into a dynamic, adaptive capability that improves with use. The fastest-improving AI systems are not those that started best — they are those that learn most efficiently from production. Building feedback loops is how we ensure AI performance improves Sooner rather than degrading quietly.

Our Expectation Every production AI system has documented feedback mechanisms, a defined retraining cadence informed by that feedback, and metrics tracking improvement over time. AI systems that accumulate production experience without learning from it are wasting the most valuable training data available. Using feedback loops deliberately is how we build AI that gets better — Sooner — rather than systems that peak at deployment and decline thereafter.