Researchers and developers are increasingly adopting a new architecture known as the LLM Wiki pattern, originally pioneered by AI researcher Andrej Karpathy. This approach addresses a fundamental limitation in conventional document retrieval and generative AI workflows: the lack of persistent memory. Traditional systems operate like isolated search engines, resetting context with every query and failing to retain or build upon previously ingested information. The LLM Wiki pattern replaces this ephemeral retrieval model with a self-sustaining knowledge graph that compounds value over time. The architecture treats the knowledge base as a living codebase. Rather than relying solely on vector retrieval, the system uses a large language model to autonomously generate, link, and maintain a directory of interlinked Markdown pages. A standard implementation requires a minimal directory structure comprising a raw folder for unprocessed documents, a wiki directory for model-managed content, a dynamic index file, a chronological log, and a central schema file that dictates formatting, naming conventions, and workflow rules. Operationally, the system follows a continuous three-stage cycle. Users deposit source materials into the raw directory and prompt the language model to ingest them according to the defined schema. The model then summarizes content, establishes semantic connections between entities, and updates the central index and maintenance logs. Subsequent queries direct the model to consult the index first, navigate relevant pages, and synthesize answers. Periodic linting routines automatically identify orphaned pages, outdated claims, or logical contradictions, ensuring structural integrity without manual intervention. This methodology fundamentally shifts the human role from content editor to strategic curator. While traditional retrieval-augmented generation systems repeatedly reprocess documents for each interaction, the LLM Wiki compiles knowledge once and continuously refines it. The compounding effect transforms the repository into a persistent research assistant that retains contextual relationships across datasets. Visual navigation tools become highly effective because they map genuine semantic relationships rather than isolated document fragments. Early adopters report that the pattern accelerates insight generation once a critical mass of linked content is established. Implementation requires only a few hours to configure, but sustained value depends on consistent model-driven maintenance rather than manual curation. As organizations scale their internal knowledge operations, self-maintaining wiki architectures are positioned to replace static document repositories with dynamic, context-aware knowledge engines.