AI for Coding: How to Stop Being a Lucky Vibe Coder

Practical Copilot strategies to turn your AI-generated chaos into maintainable code

LLMs have consistently demonstrated that they work well for AI-assisted coding. I have written a lot about it before. AI-assisted coding, however, is very different from what I call vibe coding. A vibe coder is a person who understands neither coding nor the principles of how AI works. They simply prompt an LLM to write a website, and the model outputs some code. Sometimes it works fine — I call this person a lucky vibe coder — but often it doesn’t, and the result is horrendous code.

I often hear arguments like, “But I managed to build XYZ in two lines!” Good for you, lucky vibe coder. The problem is that this luck doesn’t last. As soon as one starts adding features, debugging, and trying to build something productive, the errors snowball. The lucky vibe coder quickly turns into a frustrated generator of AI slop.

This article explores best practices for AI-assisted coding, using GitHub Copilot Agents as an example.

1. Make sure your repository is indexed

Indexed means that your code is added to a local or remote search engine. The search engines used by coding copilots do not only add plain text (as with, say, typical RAG bots or Google search); they also capture structure such as the project tree, classes, functions, and variables.

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Further in the post: practical tips how to setup your copilot to prevent guessing and errors accompanied by a technical explanation why it works

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