What is 'prompt rot' and what causes it to silently degrade production LLM applications over time?

**Prompt rot** is when a prompt that used to work well in production starts producing worse outputs over time, even though nobody changed the prompt itself. The code is the same, but the answers get worse. There are three usual culprits. First, the model behind the API changes. OpenAI, Anthropic, and Google all ship updates that shift behavior in subtle ways. Second, the data flowing into the prompt shifts. If you do RAG, the documents in your index change as you ingest new content, and the prompt's assumptions about what context looks like quietly stop holding. Third, your users start asking new things. A prompt designed for refund questions starts seeing returns, shipping, and gift-card questions. None of these show up in a unit test. The only way to catch rot is to run a regression eval on a representative golden set, on a schedule, and watch the curves.

**Prompt rot** is the silent quality degradation of a production prompt without any code change. The file is identical to what shipped on day one, yet output quality slides over weeks or months. Three drivers cause it. Model version drift: the provider pushes a quiet upgrade and behavior shifts, especially around format strictness and refusal thresholds. Retrieval-distribution drift: the documents your RAG pipeline pulls in change as you ingest new content, so the chunks the prompt sees no longer match what it was tuned for. Usage drift: users discover the product and start asking questions the prompt was never built for, expanding the input distribution. The failure mode is that none of this is visible without continuous evaluation. Unit tests pass because they pin specific inputs. The fix is regression CI on a representative golden set of 100 to 500 cases. Bootstrap CIs on the metrics, and sample production traces into a recent-failures bucket that feeds back into the eval set. Run on every prompt change and on a schedule, so model-side drift and data-side drift both trigger alerts before users notice.