Wiki LLM & LLM Knowledge Bases Without RAG

This video explains how Wiki LLM systems are changing research workflows by turning raw data into structured LLM knowledge bases. Inspired by Andrej Karpathy’s approach, it shows how large language models compile markdown files into interconnected knowledge systems using tools like Obsidian. Instead of relying on RAG pipelines, this method uses context windows, indexing, and summaries to enable powerful querying across personal research data. You’ll see how LLM knowledge bases ingest, organize, and expand information automatically, creating a self-improving AI wiki. This approach highlights a shift toward local, scalable, and continuously evolving knowledge systems powered by LLMs. Timestamps: 0:00 Shift from code generation to Wiki LLM knowledge systems 0:18 Data ingest process for building LLM knowledge bases 0:52 LLM compiling markdown into structured AI wiki 1:28 Obsidian as frontend for LLM knowledge base 2:05 Querying LLM knowledge bases without RAG 2:42 Generating outputs as markdown slides and visuals 3:18 LLM linting and knowledge base health checks 4:00 Scaling Wiki LLM systems and future direction Wiki LLM systems redefine how knowledge is stored, queried, and expanded by turning static files into active intelligence. LLM knowledge bases combine markdown structure, automated indexing, and continuous refinement to create compounding research systems. This model enables deeper insights, faster retrieval, and long-term knowledge leverage without relying on complex external infrastructure. #WikiLLM #LLMKnowledgeBase #AIResearchWorkflow