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Dev.to
Why AI-Generated Code Makes You Slower (And How to Fix Your Workflow)
AI coding assistants promise a productivity boost, but many developers experience diminishing returns after an initial spike. The core problem is that generated code often uses nonexistent parameters that pass silently due to dynamic typing and **kwargs. This leads to hard-to-detect bugs and increased debugging time. The article argues that the fix lies not in better AI, but in better workflow: writing tests before generating code, including library versions in prompts, breaking features into single-function requests, and distrusting convenience parameters. These practices shift verification from human review to automated tests, catching hallucinations early. The broader implication is that AI tools amplify existing good practices but cannot replace them. Without a disciplined workflow, the speed gains are temporary and replaced by a slower debugging tail.
ExperimentConfidence: MediumImmediate horizon
Published 10 May 2026, 16:51 UTC4 min read
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Summary
TL;DR
- AI code often calls nonexistent API parameters that pass silently via **kwargs.
- Static type checkers fail to catch these because of dynamic dispatch.
- Reviewer fatigue increases with mostly-correct code, making hallucinations harder to spot.
- Writing a test before generating code fails immediately for wrong API usage.
- Including the library version in prompts reduces hallucinations of outdated features.
- Breaking tasks into single-function prompts yields fewer errors than large ones.
- Distrust convenience parameters that look too perfect – they are often hallucinated.
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