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Refusing to sufficiently specify a task and hoping the model guesses correctly is not being productive. Again, these models still don't really ask questions when they should. You have to explicitly tell them to.

Specifying the problem is not extra work separate from solving it. If you skip that step, the ambiguity gets pushed into the model’s assumptions. Then you get a plausible looking answer to the wrong problem and have to waste time backing out of it.

LLMs are not magic machines that can read your mind.

by iLoveOncall10 hours ago|

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My point is that it is much faster for me to solve the problem by writing the code than to write specifications detailed enough for the model to do the right thing in the right way.

by nsingh210 hours ago|

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A highly detailed specification is not what I mean here. It's closer to plugging in a few sentence descriptions (or a totally cluttered brain dump) and having the model interview you to help pin down critical details before continuing.

In my own work, it's usually been a few critical assumptions the model made silently (and I never even though of initially) that end up being the difference between passable results the first try, and me having to go back and fix things. Occasionally some questions force me to rethink the problem entirely.

I basically always begin any long-running session with this kind of brainstorming. I don't find the existing plan modes in Claude Code/Codex to be critical enough.

by reactordev9 hours ago|

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You should try transcribing while you speak. Then you can explain and articulate the task sufficiently that the model should have enough context to complete the task to your satisfaction. Since you won’t write it.

by mejutoco7 hours ago|

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This assumes someone not articulate in writing will be articulate in talking. The most likely outcome is there will be more text with the same information. One can do a little interpretative dance as well but the clearer the requirements the better the result.

by iLoveOncall8 hours ago|

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My colleagues will thank me for speaking non-stop right next to them surely.

by antonvs13 hours ago|

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> Why supply underspecified requirements in the first place?

Minimizes effort, is the obvious answer.

by cyberpunk12 hours ago|

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Poor trade off, the model is then designing a massive chunk of your solution instead of you. With a good spec, bits of typo’d pseudocode, and slightly more effort than a couple of sentences they can actually produce passable software.

I think the reason claude has so much mindshare is exactly because it’s more useful to non-developers who wouldn’t know how to describe what an api call executes to his grandmother.

For those who can, I can’t find much of a difference between them. Codex has the slight edge, but that’s all just “feels” to me.

by ben_w12 hours ago|

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You call it a poor trade off, but:

> I think the reason claude has so much mindshare is exactly because it’s more useful to non-developers who wouldn’t know how to describe what an api call executes to his grandmother.

This is exactly the benefit for most people.

Most people don't want to code the app, they just want the app.

Even people like us who do like coding, we can only think of all of these things within a domain that we already know; somebody who writes shaders for games isn't likely to know or care much about the ins and outs of database development or how healthcare privacy law and KYC interact with zero-knowledge proofs.

(Of course, if the AI knows about these things and then completely fails to make use of that knowlege, that's still a fail).