Building an AI Text Detector From Scratch

This article by Sebastian Raschka provides a hands-on tutorial for building a custom AI text detector. It covers the entire process: constructing a dataset, training a small language model (SLM) as a verifier, deploying the model locally as an API with a user-friendly browser interface, and using reinforcement learning with verifiable rewards (RLVR) to train a model that can evade detection. The project aims to explain how AI detectors work, their limitations, and serves as a case study for building scorer/verifier systems for LLMs. It includes practical code examples and discussions on false positives and the cat-and-mouse nature of AI detection.