Interesting to see the benefits and drawbacks called out.
In this regard, AI-generated code resembles an itinerant contributor, prone to violate the DRY-ness [don’t repeat yourself] of the repos visited.
So I guess previously people might first look inside their repo’s for examples of code they want to make, if they find and example they might import it instead of copy and pasting.
When using LLM generated code they (and the LLM) won’t be checking their repo for existing code so it ends up being a copy pasta soup.
If you use AI to generate code, that should always be the first draft. You still have to edit it to make sure it’s good.
Yeah, but by generating with AI you’re incentivized to skip that initial research stage into your own code base, leading you to completely miss opportunities for consolidation or reuse
