Behind the curtain: What it really takes to bring a new model online at CodeRabbit

In an article by David Loker, the process of bringing a new model online at CodeRabbit is explored in detail, emphasizing the systematic approach required beyond mere personal preference. The journey is divided into several phases: understanding the model's DNA, evaluating data over impressions, adapting to differences, rolling out from lab to live traffic, and maintaining continuous vigilance in a steady-state phase. The article underscores the complexity of model selection and the need for a structured methodology, while also reflecting on the challenges of reading AI-generated code, highlighting that debugging can be more difficult than writing code. It also mentions the capabilities of Gemini 3, an AI model used for code-related tasks, which excels in generating comprehensive changes though it maintains a house style in its writing, requiring careful scrutiny to avoid potential errors.