What Does a RAG Pipeline for Cypress Actually Look Like?

In the last post I asked whether the AI writing your Cypress tests actually knows your app. This one gets into what it looks like to give it that knowledge, with a real example I built locally. The pattern is RAG, Retrieval-Augmented Generation. At its core it is straightforward. You index your app's documents into a vector database, and at query time, the most relevant chunks are retrieved and passed to the AI as context. The AI generates a response grounded in your actual docs rather than guessing. What I Indexed for Sauce Demo I used Sauce Demo as my test app and created three docs: An API spec covering login, inventory, cart, and checkout endpoints A component doc with exact CSS selectors for every page A bug history doc covering known failure scenarios like locked out users, problem user add to cart issues, and checkout validation gaps Not everything needs to be indexed. What actually moved the needle was the component selectors and the bug history. The AI stopped guessing at butt