Retyping LLM code combats cognitive debt in software engineering

The integration of Large Language Models (LLMs) into software engineering introduces cognitive debt, where developers accept generated code without fully understanding its logic. A proposed solution involves manually retyping LLM-generated code to ensure assimilation and validation of the logic. This 'copy-retype-review' loop, particularly for complex business logic, creates cognitive friction that aids in identifying syntactic and semantic errors, and implicit refactoring opportunities. AI

IMPACT Suggests a workflow to mitigate risks associated with LLM-generated code, potentially improving software quality and developer understanding.

RANK_REASON The item discusses a methodology for using LLM-generated code, offering an opinion on best practices rather than announcing a new product or research.

Read on dev.to — LLM tag →

• boilerplate
• Business domain
• codebase
• code production
• db.session.commit()
• distributed system
• ESLint
• Large Language Models
• LLMs
• mypy
• Recursive implementation of the Gaussian filter
• software engineering
• syntactic structures

• product
• opinion

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