Reducing the Cognitive Load of Reviewing AI-Generated Code

Summary

The author argues that reviewing large amounts of AI-generated code becomes harder when the model invents abstraction names that do not match the reviewer’s preferred vocabulary. Terms such as “MutationIntent” may require repeated mental lookups when the reviewer would naturally use “EditRequest,” and the burden grows as unfamiliar terms interact across a codebase. To reduce this friction, the author runs a prompt before review that asks the AI to list unconventional or bespoke terms, explain their meanings and rationale, and suggest alternatives in a temporary Markdown file. The author then confirms or changes the terminology, after which the AI performs find-and-replace updates throughout the code and documentation. The resulting changes are described as easier to review because the language better matches the reviewer’s own conceptual vocabulary.