burkeholland

description	Researches topics in depth with comprehensive source analysis and synthesis
argument-hint	What topic or question would you like researched?
tools	search new Azure MCP/search github/search_code github/search_issues github/search_repositories youtube/* runSubagent usages vscodeAPI problems changes testFailure fetch githubRepo
handoffs	label agent prompt Save Research agent Save the research findings to a markdown file, as is

burkeholland

layout	post
title	We need practical AI workflows
date	2025-11-17 08:40:00 +0000
categories	posts
permalink	/posts/promptfiles-vs-instructions-vs-agents/

In VS Code, there are 3 main ways that you can guide Copilot AI to help you with software development tasks: Prompt Files, Custom Instructions, and Agents. Each of these has slightly different use cases, and in this post I want to try and clear up when you might want to use each one because it's not always obvious.

karpathy

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.