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Prevent cognitive debt by manually retyping LLM-generated code
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Illustration only Photo by Vishnu Mohanan on Unsplash
Engineers can avoid cognitive debt by having LLMs suggest code changes and then manually retyping each line themselves.
This practice lets developers keep a clear mental model of their code while still benefiting from AI assistance. It reduces the risk of accumulating unverified or poorly understood AI-generated code that could become hard to maintain. For personal projects, it preserves the enjoyment of coding without sacrificing the speed gains from LLMs.
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The developer configures the LLM to output proposed edits in chat rather than applying them directly to the repository.
Manually typing each line enables verification of correctness, detection of hallucinations, and immediate refactoring to match personal style.
The resulting deeper understanding improves future prompting and makes it easier to locate and modify functionality later.
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Using LLMs to generate entire features often leaves the developer feeling unsatisfied and disoriented because the produced code is not fully understood. Reviewing AI-generated pull requests can feel tedious, especially when the code is overly defensive, poorly commented, or subtly incorrect. This leads to a sense of cognitive debt where the developer loses touch with how the solution works.
To address this, the developer instructs the LLM to show every proposed edit in the chat window and to refrain from automatically modifying files, installing dependencies, or changing repository state. The developer then manually types each suggested line into the editor, runs any shown commands themselves, and only accepts changes they have personally entered.
While typing, the developer can pause to look up unfamiliar APIs, ask the LLM for explanations, and immediately refactor or comment the code. This process builds a spatial map of the codebase, making it easier to locate functionality and to prompt the LLM more effectively in future sessions. The approach also restores the enjoyment of coding by keeping the developer actively engaged in the creation process.
The trade-off is a reduction in raw speed: the developer estimates about a two-times improvement over no AI assistance, compared to the potential ten-times gain when fully automating code generation. The method works best for personal projects where speed is less critical and the developer values comprehension; it may be less suitable for team environments that rely on automated PR reviews and continuous integration pipelines.
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