Why errors and retries still use credits
Building with AI is iterative — some generations error or need a follow-up, and those still use credits. Here's why, and how OverSkill keeps the cost low.
Short version: building with AI is iterative. Some generations hit an error or need a follow-up to get right, and those still use credits — because the AI did real work to produce the attempt. This is normal for every AI development tool in 2026, not just OverSkill. Here's the honest, complete picture, plus exactly what we do to keep that cost as low as possible.
What counts as work
When you send a request, the AI reads your app's context, reasons about what to change, and writes the result. That processing is the work — and it's what credits measure. The underlying AI model providers bill us for it the moment it happens, whether the final result is perfect, imperfect, or errors out partway through.
So a generation that ends in an error still consumed real compute. The AI didn't sit idle — it read, reasoned, and wrote, right up until the problem surfaced. That's why the credits are used.
Why iteration is normal
Nobody describes exactly what they want on the first try, every time. You ask for a feature, see the result, and refine it. Sometimes a build hits a snag and needs a second pass. This back-and-forth isn't a sign something is broken — it's how building with AI works. A typical app is shaped over several rounds, and a little spend goes into the rounds that miss before they land.
We'd rather say that plainly than let it be a surprise on your balance.
What OverSkill does to minimize the waste
We go beyond the raw AI models so you spend fewer credits than you would using them directly:
- Automatic error-catching. When a build fails on a common, fixable problem, OverSkill detects it and repairs it for you — often before you even see the error. That turns what would've been a manual retry (and more of your credits) into an automatic fix.
- Token optimization. We're careful about how much your app's context the AI has to read on each request. Less to process means fewer credits per generation.
- Credit estimates up front. Before a large change runs, you see an estimate — so big asks never run a huge bill without your say-so.
- Plan mode. For bigger or fuzzier requests, plan mode has the AI think and ask questions first, so it builds the right thing once instead of the wrong thing three times.
These genuinely lower the cost of iteration. They don't make it zero — no tool can, because some of the spend is the AI doing the actual work you asked for.
How to keep your iteration cost low
A few habits make a real difference:
- Be specific.
Add a star rating to each recipe
costs less thanmake it better,
because the AI doesn't have to guess. See How to ask the AI for changes. - One change at a time. Smaller, focused asks are cheaper and easier to roll back than one giant prompt.
- Use plan mode for big asks. It spends a little up front to avoid spending a lot on the wrong approach. See Plan mode vs build mode.
- Avoid full rewrites.
Redesign the whole app
touches every page and costs accordingly. Target the part you actually want to change. - Roll back instead of arguing. If a change went sideways, restoring an earlier version is free — sometimes cheaper than four correction prompts in a row. See Version history and rollbacks.
If something feels wrong
If you hit a generation that errored in a way that looks like a problem on our end — not the normal kind of iteration described here — tell us. Open a ticket with what you were trying to do and what happened. We monitor build health closely and want to know when something isn't behaving as it should.
What to read next
- What are credits? — how the meter works
- Out of credits — what now? — top up, upgrade, or wait
- When things go wrong — fixing build and preview problems