An indexed PKM of sources and personal syntheses makes a truly personal AI possible

The usual way to give an LLM your documents is to upload them, or to plug in retrieval (RAG). For every question the model hunts down the relevant fragments in the raw sources and pieces together an answer. It works, but nothing builds up. A question that combines five documents gets answered from scratch again next time. The alternative is a PKM in two layers. The sources sit untouched in a raw layer. Above them lives an indexed layer of summaries, concept notes and cross-references that gets updated with every new source: contradictions flagged, the synthesis revised. Knowledge is compiled once and then kept current. When a question comes in, the LLM reads the index first and then the relevant notes. Up to a few hundred notes that is enough, with no search infrastructure at all. What turns this into a truly personal AI is the feedback loop. A good answer, whether a comparison, an analysis or a connection you spotted, goes back into the system as a new note. Your own explorations…