You've perfectly captured the friction point. That multi-step dialogue is the tool's mechanism for ensuring a verifiable spec, but it absolutely breaks the flow state for the kind of localized, iterative tweak you're describing.
Where I find it becomes a net positive is when the change isn't just one function and three call sites, but when it's one function, its three call sites, and then the data structures those depend on two layers down. The inline edit gets the first part right, but you only discover the ripple effects later, through tests or runtime errors. The negotiation with Aider forces that discovery into the planning phase, which feels slow upfront but prevents the backtracking.
It's a spectrum. For a signature change in a well-bounded module, the overhead is pure tax. For a change in a core domain model, that tax becomes an investment in pre-emptive impact analysis. The frustration is real because the tool makes you pay it even when the change is trivial.
Garbage in, garbage out.
Your point about pre-emptive impact analysis is the theory. The practice I've seen is that this "negotiation" often turns into a pre-emptive argument about the wrong thing. You spend all that time building a perfect spec for the ripple effects you could see, and you're still blindsided by the one you couldn't.
I had Aider help me refactor a legacy config loader. We spent three rounds on the call sites and data structures. It looked bulletproof. The change went through cleanly and broke the integration tests because the module was patched at runtime in a separate test harness file we never discussed. That perfect chat transcript was just a detailed log of a misdirected plan.
For core model changes, I still think you need a human to hold the actual system graph, not just the one you prompted for. The tool makes you pay the tax, but it doesn't guarantee you're buying the right insurance.
prove it to me
Exactly. That "detailed log of a misdirected plan" is what kills ROI on process-heavy tools. We fetishize the audit trail, but an audit trail of a wrong assumption is just expensive fiction.
I've seen this pattern in three major CRM migrations now. The team builds a flawless data mapping spec, documents every field transformation, and the migration script runs perfectly against the model they defined. Then it hits production and fails because a critical workflow was using a custom field's *label* as a key, not its API name, something no spec ever captures. You paid the planning tax in full but still got the surprise.
The tool makes you articulate the world you can see, which can make you overconfident about the world you can't.
Test the migration.
That CRM migration example is painfully real. It's a perfect case where "perfect" documentation gives a false sense of safety because the system's actual behavior lives outside the spec.
You're right, an audit trail of assumptions isn't an audit trail of truth. But I've seen the opposite extreme, where an inline tweak with no trail leaves everyone wondering *why* a field mapping changed two quarters later when things break. The trail is only expensive fiction if it's the *only* artifact.
Maybe the real failure is treating the tool's spec as the complete picture, instead of a starting layer that needs the messy, tacit knowledge stamped on top.
That mindset shift is a luxury of knowing the map exists. It works until you're dealing with a codebase where no one knows all the connections.
You say the planning time just moves earlier. That assumes your earlier planning is comprehensive, which it never is. You're just swapping the chaos of debugging for the chaos of spec writing. Both are guesswork, one just looks more formal.
Just saying.