Erick Cesar ecesar88

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

	" VimPlug START
	" Specify a directory for plugins
	" - For Neovim: stdpath('data') . '/plugged'
	" - Avoid using standard Vim directory names like 'plugin'

	" Autoinstall vim-plug if it isn't installed already
	if empty(glob('~/.config/nvim/autoload/plug.vim'))
	silent !curl -fLo ~/.config/nvim/autoload/plug.vim --create-dirs
	\ https://raw.githubusercontent.com/junegunn/vim-plug/master/plug.vim
	autocmd VimEnter * PlugInstall

	export const logFetchingSingleRecord = (id: string, obj?: Record<string, unknown>) => {
	logger({
	level: LOG_TYPE.INFO,
	message: `Fetching client with id '${id}'`,
	...(obj ? ({
	object: colorizeAsJSON(obj),
	}) : ({}))
	});
	}