jcoding09

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.

Prologue

I wrote this answer on stackexchange, here: https://stackoverflow.com/posts/12597919/

It was wrongly deleted for containing "proprietary information" years later. I think that's bullshit so I am posting it here. Come at me.

The Question

Amazon is a SOA system with 100s of services (or so says Amazon Chief Technology Officer Werner Vogels). How do they handle build and release?

	// 22 July 2022
	// Save() function for saving your console script experiments.

	// See the long explanation on this gist at
	// https://gist.github.com/dfkaye/d3c7d0d59cb03616ad2eee8eec1141a5#file-save-your-console-work-js

	// Save() takes an array of script functions and fragments,
	// and prints them as a single text block to the console,
	// and attempts to download the text block to your browser's
	// downloads folder.