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Why spec-driven development: LLMs are over-eager AI interns

From Using Spec-Driven Development for Production Workflows - Erik Hanchett, AWS · ≈1:03

Frames the core failure mode spec-driven development addresses: coding agents execute instantly on underspecified asks, so structure and written specs are the guardrail, not better models.

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  • Frames the core failure mode spec-driven development addresses: coding agents execute instantly on underspecified asks, so structure and written specs are the guardrail, not better models.
Clip transcript
show you exactly how that works. Now, a question I sometimes get is why? Like, why should we use this spec-driven development workflow and then how can we do it? So, to answer that why question, I kind of like to think about our coding assistants, our large language models that we're using every day as sort of like AI interns. You need to really prompt them, you really need to push them the right way, no pun intended with the prompting. So, you really need to guide them. Because if you give them just a little bit of of leeway, they will go off the rails. And really the spec-driven development helps guide them in the right direction. I remember when I was an intern in one of my first jobs, we had a VP that would walk around and he would just come up with random ideas off the top of his head. And when he told me it, I would drop everything and work on it. And then later my boss would be like, "Hey Eric, why did you drop everything?" Well, the the VP said something. And then I learned about, well, you got to take his information that he gives you, write it down, put it in your schedule, talk to me talk to my manager about it. So, I was a little too eager. And this is what AI interns or basically these large language models do nowadays. And we got to be careful about
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