Every RAG system I've seen — including the one I wrote a handbook about on this site — has the same fundamental problem.

It doesn't learn.

You ingest 500 documents. You ask a question. The system retrieves the three most similar chunks and hands them to the LLM. Repeat for the next query.

The system knows exactly as much as it did on day one. It's a library that never builds a card catalog, never cross-references its own shelves, never notices that three of its books are saying contradictory things.

That's what I set out to fix with a knowledge reflection layer. After every ingest, the system finds semantically related documents already in the index and asks an LLM to synthesise what's new, how it connects, and what gap remains.…