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.
The article discusses the app Noteey, which transforms how users learn, brainstorm, and remember by allowing them to create dynamic, visual maps of their knowledge. It highlights features like infinite canvas boards, versatile cards, PDF editing, digital journaling, local storage, and multiple use cases, making it suitable for various tasks from project management to creative writing.
