First, hit the logs. See if the sentiment model is getting the correct labels on the training set, and check the tokenization pipeline—an off‑by‑one error there can flip a whole batch. If the raw inputs look fine, run a single sentence through the entire inference chain and compare the intermediate embeddings to a known good run. That’s the quickest way to spot a drift in the model or a mis‑configured preprocessing step.