Who thinks document "chunking" sucks and why?

All the "memory" systems/pipelines for LLMs seems to be using the exact same approach:

1. Chunk Docs + Embed 2. Store in a VectorDB 3. Query embeddings based on semantic relevance

In my work, this has consistently failed to get meaningful context for prompts. Who has seen a better way of handling this problem?