Output drift & performance degradation
Model Accuracy & ReliabilityDescription
Model or pipeline changes (provider updates, prompt edits, data shifts) silently degrade output quality over time.
A silent provider model update degrades the summariser, and errors accumulate before anyone notices.
The 'Lost in the Middle' paper (Liu et al., 2023) empirically demonstrated that transformer models systematically underweight information positioned in the middle of long contexts, confirming silent information loss as a real model behaviour. This has been reproduced across multiple models and context lengths, but specific production incidents where context truncation caused documented decision-making harm in a financial or legal workflow have not been publicly confirmed.
Primary mitigations
- Continuous evaluation harness
- regression test suites
- canary prompts
- change gating
- provider-version pinning.
Detection signals
Eval-score trend monitoring; canary regression alerts; drift dashboards.