As far as I understand it, context length degrades llm performance, so just because an llm "supports" a large context length it basically just clips a top and bottom chunk and skips over the middle bits.

Why would you want chunks that big for vector search? Wouldn't there be too much information in each chunk, making it harder to match a query to a concept within the chunk?

The problem is that often semantic meaning depends on state multiple paragraphs or sections away.

This is a coarse way to tackle that