• embedding — A list of numbers representing a piece of text's meaning, so that similar meanings end up numerically close and can be searched.
• inference — Running a trained model to get answers — the phase where AI is actually used, as opposed to trained.

Shows how to keep embedding-based ranking inside tight latency budgets by moving inference to nearline pre-computation and publishing embeddings as features, instead of running the model on the request path.

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