L arge L anguage M odels (aka LLMs ) have a memory problem: their knowledge stops the day their training data was cut off, they don't know your codebase, they don't know last week's tickets… When they're missing context they don't say so… they guess, confidently. The polite term is hallucination ; the less polite one is lying with style . R etrieval- A ugmented G eneration (aka RAG ) is how you fix that without retraining anything. Think of it as turning a closed-book exam into an open-book one. The LLM is still the writer, but now it has a librarian: a system that fetches the right passages from your data and hands them over before the model puts pen to paper. I built Keystone to learn this end-to-end. Keystone does two things: Ingest a GitHub repository's activity → every PR, commit, issue, and discussion Answer questions about why the codebase looks the way it does . The first prototype didn't have a retrieval system, it had a giant string. That worked for tiny repos.…