Does semantic chunking (splitting on meaning boundaries rather than fixed size) actually pay off in practice, or is it overkill?

It depends on the corpus, and treating it as a universal upgrade is a mistake. Semantic chunking — using embedding similarity or topic-shift detection to decide chunk boundaries rather than a fixed character count — measurably improves retrieval precision specifically on documents where topic density varies a lot within a page: long-form prose, transcripts, or documents that don’t have reliable heading structure to split on instead.

On documents that are already well-structured — clear headings, fairly uniform section lengths, consistent formatting — the measurable gain from semantic chunking over simple structure-aware splitting (chunk on headings, add overlap) shrinks toward negligible, while you still pay the extra embedding computation and pipeline complexity semantic chunking requires. The practical takeaway: it’s worth A/B testing semantic chunking against simpler structure-based chunking on your actual document set before adopting it by default, rather than assuming “more sophisticated” automatically means “better retrieval” for your specific corpus.