Your RAG System Can Be Healthy and Still Be Wrong
When an AI application gives an incorrect answer, the first instinct is often to inspect the model. Engineers review the prompt, switch models, adjust temperature, or experiment with a larger context window. Sometimes that is necessary. But there is another possibility: the model answered correctly using information that should no longer have been trusted.
Consider a policy assistant used inside an enterprise. A policy is updated at 10:00 AM, but the retrieval index still contains the previous version. At 10:05 AM, an employee asks a question about the new policy. The retrieval service responds successfully, the language model generates a coherent answer, and the application returns HTTP 200.
From an infrastructure perspective, the request appears healthy. From a business perspective, the answer may be wrong.
This is one of the difficult problems in production AI: a system can complete every technical step successfully and still produce an invalid outcome. The...
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