It's going to backfire. In real scenarios (not regression testing) users don't want to see the exact same thing twice out of the LLM in the same session in spite of trying to refine the result with more context.

There are going to be false positives: text that is subtly different from a previous response is misidentified as a duplicate such that the previous response is substituted for it, frustrating the user.

Not an expert, but I've been told RAG in combination with a database of facts is one way to get more consistency here. Using one of the previous examples, you might have a knowledge store (usually a vector database of some kind) that contains a mapping of countries to capitols and the LLM would query it whenever it had to come up with an answer rather than relying on whatever was baked into the base model.