Reducing the cognitive load of AI changes
When evaluating extensive AI-generated code, I frequently encounter the large language model selecting abstraction terms that do not align with my own preferences. For example, it might label a MutationIntent as an EditRequest, a term that feels more intuitive to me. However, since the LLM's choice of words doesn't match my ideal, I must mentally verify the meaning each time I come across it, which adds an extra cognitive burden.
This seemingly minor inconvenience can quickly accumulate when dealing with numerous unfamiliar terms interacting within the code. My brain can only juggle a limited number of semantic lookups before I begin misinterpreting their functionality. To alleviate this, I employ a pre-review process that involves this prompt: I then proceed to confirm term choices.
The AI conducts a global find-and-replace, encompassing all documentation. As a result, the revised code becomes much more comprehensible during review, as it now aligns more closely with my mental framework.
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