The Struggle to Maintain Ownership Over AI-Generated Code
The author shares their experience of using AI to write code, feeling disconnected from the codebase, and developing a workflow to maintain control and understanding.
Why it matters
This article highlights the challenges developers face in maintaining ownership and understanding of AI-generated code, and the importance of finding the right balance between AI assistance and manual coding.
Key Points
- 1The author felt disconnected from the codebase when letting AI write code fully autonomously
- 2They experienced a 'burn' incident where the AI-generated code diverged from their intent
- 3The author developed a workflow where they write the core logic and architecture, and use AI for deterministic functions
- 4The author values maintaining a 'machine spirit' or connection to the codebase, even if it means sacrificing some speed
Details
The author describes their experience of using AI to write code, where they felt a disconnect from the codebase and an inability to understand the code they had generated. They explain that reviewing code is not the same as understanding it, and that the 'intention gap' is real. After a 'burn' incident where the AI-generated code diverged from their intent, the author developed a workflow where they write the core logic and architecture by hand, and use AI to handle the more deterministic functions. They value maintaining a 'machine spirit' or connection to the codebase, even if it means sacrificing some speed. The article also discusses feedback from the developer community, including perspectives on using AI for greenfield vs. established codebases, and the importance of building code in stages rather than generating large 'blobs'.
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