Some examples for instructions:
- How to translate named entities and technical terms (e.g., "Big Data," "Embeddings")
- Specifying date formats (MM/DD/YY, DD/MM/YY, YYYY-MM-DD)
- Define tone of a text (e.g., formal vs informal)
Nevertheless LLM's latency might be much higher.

Whether to use late chunking also depends on the chunk size, for smaller chunks late chunking is generally more useful than for large chunk sizes.

Chunking improves the performance for fact retrieval task but can actually harm the performance for other retrieval tasks. Late chunking is useful for coherent datasets and often a good compromise to help embeddings to retain context information but also to focus on details:

First, more input helps, but not for all retrieval tasks equally: