Common Mistakes in AI-Assisted Development

The common mistakes in AI-assisted development have almost nothing to do with the model. The model is fine. The mistakes are process: people prompt before they plan, ship the demo as if it were the product, re-prompt from scratch when they should correct in place, and give the model no way to tell whether it succeeded. Fix those four and AI-assisted development goes from frustrating to fast. Keep making them and no model, however good, will save you.

I have made all of these. Here is what they cost and how to stop.

Mistake one: prompting before planning

This is the big one and it causes most of the rest. You open a chat, describe what you want, and let the model start building before you have decided what building means. The model fills every gap you left with its own defaults, and its defaults are the average of every codebase it trained on. You end up owning architecture you never chose.

The fix is to write the spec first. Decide the entities, the routes, the states, the definition of done, then prompt. I made the full case in write the spec before the prompt. The plan is the cheapest place to be wrong. Skip it and you move your wrongness into the code, where it costs ten times as much to fix.

Mistake two: trusting the demo

The demo works. You show it, it does the thing, everyone is impressed. Then you try to extend it and discover there is nothing underneath. A demo proves the happy path runs once. It proves nothing about the error cases, the edge states, the load, or whether the next feature can be added without a rewrite.

Treating the demo as shippable is how vibe-coded projects hit a wall. I wrote about that exact gap in demo to shippable. The rule: a demo is a question, not an answer. It asks "does this look right." Shippable software answers "does this hold up." Do not confuse the two, and never ship the first one thinking it is the second.

Mistake three: re-prompting instead of correcting

The model produces code with a bug. Instead of handing it the exact error and the exact code and asking for the minimal fix, people delete the whole thing and prompt again from scratch. They get a different set of bugs, delete again, prompt again. This is the spin, and it can eat a whole day.

Full rewrites oscillate. Small corrections converge. When something fails, feed the failure back precisely: here is the code, here is the error, fix this. The model patches the specific problem and you move on. This is one step of the build loop, the correct step, and it is the step people most often replace with panic.

Mistake four: no definition of done

The model generates, and you have no way to say whether it worked, because "make it good" is not testable. Without a definition of done, the verify step is impossible, so you either accept output you did not check or check it by eyeballing, which misses everything that is not on screen.

Every plan needs a testable done before you generate a line. Tests that pass or fail. A build that succeeds or errors. A feature that runs or does not. Give the loop something to verify against and it can tell you the truth. Give it nothing and it can only tell you the code exists, which you already knew.

The mistake under all the mistakes

Every one of these is the same error wearing a different hat: treating AI-assisted development as prompt-and-pray instead of as a disciplined loop. Plan, generate, verify, correct, repeat. Skip the plan and you get mistake one. Skip the verify and you get mistakes two and four. Skip the correct and you get mistake three. The discipline is not optional overhead. It is the difference between AI making you fast and AI making you a fast producer of mess.

I run the disciplined version on Bootspring, where the spec, the generation, and the checks live in one loop so the process is the tool, not a set of habits I have to remember. When the platform enforces the loop, the four mistakes get much harder to make. The model was never the problem. The process was. Fix the process.