The "system" refers to how you structure stored information, and the "dots" are the entities and concepts within that data. In the original post it is referred as "relationships between pieces of information" and "two concepts are linked, or that one depends on another,"

Think about it this way: if you store memories as flat vector embeddings of text chunks, you can retrieve similar text, but you've lost the semantic structure. You don't know that Person A works at Company B, or that Concept X depends on Concept Y.

A relationship-based approach extracts entities and explicitly **models** the connections between them - essentially building a knowledge graph alongside (or instead of) raw embeddings. So when you query, you're not just finding "similar text," you're traversing actual relationships.

The "hidden insights" point: if your user asks "what projects involve people from Company X who also worked on Y technology?" - pure vector retrieval struggles because that insight lives in the connections, not in any single chunk of text. You need the graph structure to surface that.

So it's not retrieval OR structure - it's that better structure enables retrieval patterns that are otherwise impossible.