AI-assisted coding for teams that can't get away with vibes (via) This excellent piece by Atharva Raykar offers a bunch of astute observations on AI-assisted development that I haven't seen written down elsewhere.
Building with AI is fast. The gains in velocity are important, because when harnessed correctly, it allows teams to tighten feedback loops with users faster and make better products.
Yet, AI tools are tricky to use. Hold it wrong, and you can generate underwhelming results, worse still, slow down your velocity by drowning your project in slop and technical debt.
Atharva notes that AI is a multiplier: the more expertise you have in software engineering, the better the results you can get from LLMs. Furthermore, what helps the human helps the AI.
This means good test coverage, automatic linting, continuous integration and deployment, good documentation practices and "clearly defined features, broken down into multiple small story cards".
If a team has all of this stuff in place, AI coding assistants will be able to operate more reliably and collaborate more effectively with their human overseers.
I enjoyed his closing thoughts about how heavier reliance on LLMs changes our craft:
Firstly, It’s less valuable to spend too much time looking for and building sophisticated abstractions. DRY is useful for ensuring patterns in the code don’t go out of sync, but there are costs to implementing and maintaining an abstraction to handle changing requirements. LLMs make some repetition palatable and allow you to wait a bit more and avoid premature abstraction.
Redoing work is now extremely cheap. Code in the small is less important than structural patterns and organisation of the code in the large. You can also build lots of prototypes to test an idea out. For this, vibe-coding is great, as long as the prototype is thrown away and rewritten properly later. [...]
Tests are non-negotiable, and AI removes all excuses to not write them because of how fast they can belt them out. But always review the assertions!
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