Context rot
Context rot is the gradual decline in a large language model's reliability as the conversation or context window gets very long. As more tokens accumulate, the model attends less consistently to earlier instructions and facts, so it starts forgetting constraints, contradicting earlier decisions, or reintroducing problems that were already resolved.
Also known as: context degradation
In AI app builders this shows up as a long-running project where the assistant increasingly ignores your established conventions or breaks working features. Practical mitigations include starting fresh sessions, restating the key constraints, and keeping individual tasks small and self-contained rather than relying on a single ever-growing thread.
In a large Lovable project context rot is a frequent reason a build that worked early starts regressing as it grows. Common mistake: pushing every change through one enormous, never-ending chat — instead, scope tasks tightly and restate the constraints that matter, so the model is not relying on hundreds of earlier messages it can no longer attend to reliably.
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