The requirement is to ensure embeddings are updated every time the underlying content changes without relying on a nightly batch job. The right design is to enable change tracking on the source table so an external process can identify which rows changed and regenerate embeddings only for those rows. Microsoft documents that change detection mechanisms are used to pick up new and updated rows incrementally, which is the right pattern when you need near-continuous refresh instead of full nightly rebuilds.

This is better than:

A . fixed-size chunking, which affects chunk strategy but not change detection.

B . a smaller embedding model, which affects model cost/latency but not update triggering.

C . table triggers, which would push embedding-maintenance logic directly into write operations and is generally not the best design for AI-processing pipelines. The question specifically asks for a solution that replaces the nightly batch requirement, not one that performs heavyweight work inline during every transaction.