Writing detailed specs and then giving them to an AI is not the optimal way to work with AI.

That's vibecoding with an extra documentation step.

Also, Sonnet is not the model you'd want to use if you want to minimize cleanup. Use the best available model at the time if you want to attempt this, but even those won't vibecode everything perfectly for you. This is the reality of AI, but at least try to use the right model for the job.

> Therefore I need more time and effort with Gen AI than I needed before

Stop trying to use it as all-or-nothing. You can still make the decisions, call the shots, write code where AI doesn't help and then use AI to speed up parts where it does help.

That's how most non-junior engineers settle into using AI.

Ignore all of the LinkedIn and social media hype about prompting apps into existence.

EDIT: Replaced a reference to Opus and GPT-5.5 with "best available model at the time" because it was drawing a lot of low-effort arguments

> Writing detailed specs and then giving them to an AI is not the optimal way to work with AI.

It is NOT the way to work with humans basically because most software engineers I worked with in my career were incredibly smart and were damn good at identifying edge cases and weird scenarios even when they were not told and the domain wasn't theirs to begin with. You didn't need to write lengthy several page long Jira tickets. Just a brief paragraph and that's it.

With AI, you need to spell everything out in detail. But that's NO guarantee either because these models are NOT deterministic in their output. Same prompt different output each time. That's why every chat box has that "Regenerate" button. So your output with even a correct and detailed prompt might not lead to correct output. You're just literally rolling a dice with a random number generator.

Lastly - no matter how smart and expensive the model is, the underlying working principles are the same as GPT-2. Same transformers with RL on top, same random seed, same list of probabilities of tokens and same temperature to select randomly one token to complete the output and feedback in again for the next token.

> Opus or GPT-5.5 are the only ways to even attempt this.

It’s pretty funny to claim that a model released 22 hours ago is the bare minimum requirement for AI-assisted programming. Of course the newest models are best at writing code, but GPT-* and Claude have written pretty decent systems for six months or so, and they’ve been good at individual snippets/edits for years.

> Stop trying to use it as all-or-nothing. You can still make the decisions, call the shots, write code where AI doesn't help and then use AI to speed up parts where it does help.

You're assuming that finding the places where AI needs help isn't already a larger task than just writing it yourself. AI can be helpful in development in very limited scenarios but the main thrust of the comment above yours is that it takes longer to read and understand code than to write it and AI tooling is currently focused on writing code.

We're optimizing the easy part at the expense of the difficult part - in many cases it simply isn't worth the trouble (cases where it is helpful, imo, exist when AI is helping with code comprehension but not new code production).

> Writing detailed specs and then giving them to an AI is not the optimal way to work with AI. > That's vibecoding with an extra documentation step.

Read uncharitably, yeah. But you're making a big assumption that the writing of spec wasn't driven by the developer, checked by developer, adjusted by developer. Rewritten when incorrect, etc.

> You can still make the decisions, call the shots

One way to do this is to do the thinking yourself, tell it what you want it to do specifically and... get it to write a spec. You get to read what it thinks it needs to do, and then adjust or rewrite parts manually before handing off to an agent to implement. It depends on task size of course - if small or simple enough, no spec necessary.

It's a common pattern to hand off to a good instruction following model - and a fast one if possible. Gemini 3 Flash is very good at following a decent spec for example. But Sonnet is also fine.

> Stop trying to use it as all-or-nothing

Agree. Some things just aren't worth chasing at the moment. For example, in native mobile app development, it's still almost impossible to get accurate idiomatic UI that makes use of native components properly and adheres to HIG etc

Sure, Opus is next level than Sonnet, but it still doesn't free OP from these handcuffs - It is reading the code, understanding it and making a mental model that's way more labour intensive.

Funny hearing you’re saying only GPT 5.5 (and Opus) can do this, having in mind that it came out last night.

"Writing detailed specs and then giving them to an AI is not the optimal way to work with AI." Perfect. I loosely define things, and then correct it, and tell it to make the corrections, and it gets trained, but you have to constantly watch it. Its like a glorified auto-typer.

"Ignore all of the LinkedIn and social media hype about prompting apps into existence." Absolutely, its not hype, its pure marketing bullshitzen.