This article explores a practical approach to building an LLM knowledge base by treating the model as a compiler rather than just a retrieval tool. Instead of relying solely on complex RAG systems and vector databases, the author proposes a structured workflow that transforms raw source material into a durable, organized wiki. This method focuses on creating lasting value through repeatable processes like indexing, compiling paper pages, developing concept maps, and filing query answers back into the system to create a continuous feedback loop.
Main points:
- Moving beyond traditional RAG toward an LLM-driven compilation workflow.
- Implementing a structured folder hierarchy including raw, wiki, derived, and prompts directories.
- The importance of creating concept pages that connect multiple sources rather than just summarizing individual papers.
- Establishing a feedback loop where query answers are saved back into the knowledge base.
- Using maintenance passes to ensure the system remains updated and cohesive.
Main points:
- Moving beyond traditional RAG toward an LLM-driven compilation workflow.
- Implementing a structured folder hierarchy including raw, wiki, derived, and prompts directories.
- The importance of creating concept pages that connect multiple sources rather than just summarizing individual papers.
- Establishing a feedback loop where query answers are saved back into the knowledge base.
- Using maintenance passes to ensure the system remains updated and cohesive.
