You read a research paper. Three weeks later, a question comes up. You can't remember the answer. You search your notes, find nothing useful, and paste the PDF into your AI assistant again. Same cost, same latency, and depending on the day, a slightly different answer. This is the trap in naive RAG workflows: the system retrieves raw context, then re-synthesizes the answer on every query. For dynamic document stores, that's the right call. But for knowledge that doesn't change (research papers, architecture decisions, API conventions, meeting outcomes) you're paying the synthesis cost over and over for no reason. Andrej Karpathy proposed a better pattern llm-wiki.md : compile the knowledge at ingest time. The LLM reads a document once, writes structured wiki pages, and future queries draw on pre-built knowledge. No regeneration. No inconsistency. The catch: you have to build it yourself.…