You are tuning an Azure AI Foundry chat deployment for a marketing copy generator. Reviewers complain that outputs are too repetitive and formulaic across generations, but you must avoid producing incoherent or off-topic text. You decide to keep temperature at its current moderate value and instead adjust nucleus sampling to widen the pool of candidate tokens while still discarding the long tail of very unlikely tokens. Which parameter change should you apply?