# AI-assisted development

AI-assisted development is writing software with AI coding tools that suggest, generate, and edit code, while engineers decide the architecture, review every change, and stay responsible for how the software behaves.

Also called: AI-assisted coding, AI pair programming, AI-accelerated development.

## In short

- AI tools write and edit code quickly. Engineers still design the system, review the changes, and own the result.
- The biggest gains come on well-understood work such as boilerplate, tests, refactors, and familiar features.
- Architecture, security, and deciding what to build do not speed up in the same way.
- Code written with AI help is ordinary code in your repository. Your contract decides who owns it.

## What AI-assisted development means

The term covers a wide range of tools. GitHub Copilot, Cursor, and Claude span everything from suggesting the next few lines as an engineer types to reading a whole codebase, changing many files at once, running the tests, and explaining what changed. What makes the work assisted is that an engineer directs it and reads what comes back.

At the other end sits [vibe coding](https://www.choie.co/glossary/vibe-coding), where someone describes what they want and accepts the code without reading it. Both use the same tools. The difference is whether a person with engineering judgment checks the work before it reaches users.

## How it works on a real project

On a well-run project, the engineer breaks the work into small, clear tasks and gives the tool the context it needs, such as the relevant files, the project's conventions, and the goal. The result gets reviewed like a pull request from a new colleague. Tests and type checks catch some mistakes automatically, and a person who understands what the code should do catches the rest. At Choie, AI drafts implementation from a design that is already decided, and generated code that nobody understands is not merged.

**Diagram: One change, AI-assisted.**

1. Plan the task (Engineer sets the scope and context)
2. AI drafts the change (Code, tests, and an explanation)
3. Review the diff (Read it like a colleague's pull request)
4. Run the checks (Tests, types, and a real device)
5. Ship (Small change, easy to undo)

_The AI step is fast. The steps around it are what keep the code fit for production._

## What gets faster, and what still needs people

| Work | How AI helps | What a person still does |
| --- | --- | --- |
| Boilerplate and setup | Writes it in minutes | Chooses the patterns it should follow |
| Familiar features | Drafts forms, lists, and standard screens from a short description | Checks edge cases, states, and data rules |
| Tests | Proposes cases and writes them | Decides what must be tested and checks the tests catch real failures |
| Refactoring | Renames and restructures across many files | Confirms the behavior did not change |
| Architecture | Suggests options and explains trade-offs | Makes the call, because it lasts for years |
| Security and privacy | Flags some common issues | Owns access rules, secrets, and how data is handled |

The pattern is consistent. AI helps most where the right answer is already well known and the code is easy to check. It helps least where the hard part is a decision, such as the data model, who can see what, or what the product should do at all.

## Effect on cost and time

Faster code generation shortens builds, mostly in the middle of a project where a lot of standard code gets written. It does not shorten the time it takes to agree on what to build, wait for feedback, review changes, or test on real devices. The saving is real but uneven, and it is largest when the scope is clear and the team is senior enough to review quickly.

The risk on the cost side is hidden debt. Code produced quickly and never properly reviewed becomes [technical debt](https://www.choie.co/glossary/technical-debt), and that bill arrives later as bugs, slow changes, and rewrites. Our guide to [AI-accelerated development](https://www.choie.co/guides/ai-accelerated-development) covers how it changes price and time, and what to ask a studio that works this way.

## Common mistakes

- **Shipping code nobody read.** This is where assisted development slides into vibe coding. Every change that ships should be read by someone who could have written it.
- **Asking for too much at once.** Large, vague requests produce large, vague changes that are hard to review. Small tasks get better results.
- **Letting the tool choose the architecture.** It will pick something plausible. Plausible is not the same as right for your data and your plans.
- **Trusting code because it looks finished.** Generated code often reads well and still has gaps. Tests and real use are how you find them.
- **Pasting secrets or customer data into prompts.** Check what each tool stores and where it sends data before using it on private code.

## Who owns code written with AI

Code an engineer writes with an AI tool goes into your repository like any other code, and your contract decides who owns it. Check that the agreement assigns the source code to you once you have paid, and that the team uses its tools under terms that allow commercial work. The law on copyright in AI-generated material is still developing, so if the code itself is a core asset, ask a lawyer. Our guide on [who owns the code](https://www.choie.co/guides/who-owns-the-code) lists what to look for.

Choie handles ownership per deliverable. Once a deliverable's fee is paid, the designs and source code written for the client are theirs to use, modify, and distribute, whether a person or a tool typed them. Third-party libraries and fonts keep their own licenses. The [pricing page](https://www.choie.co/pricing) has the details.

## Common questions

### Is code written with AI lower quality?

Not by itself. Quality depends on the review, tests, and judgment around the code, the same as with code typed by hand. Unreviewed AI code is often worse, because it can look finished while hiding mistakes. The choie.co site is built with AI help, and Choie has a senior person read every line before it ships.

### Does AI-assisted development make software cheaper?

It can, for the same scope, because standard code gets written faster. The saving is smaller on work that depends on decisions, review, and testing, which AI does not remove.

### How is AI-assisted development different from vibe coding?

The difference is review. In AI-assisted development an engineer reads, tests, and takes responsibility for every change. In vibe coding the code is accepted without close reading, which suits a throwaway prototype and is risky for a live product.

### Who owns code written with AI tools?

Your contract decides, the same as for any other code. Make sure it assigns the source code to you, and get legal advice if the code is a core asset, because the law on AI-generated material is still developing.

Related: [Vibe coding](https://www.choie.co/glossary/vibe-coding), [AI-assisted design](https://www.choie.co/glossary/ai-assisted-design), [Technical debt](https://www.choie.co/glossary/technical-debt), [QA testing](https://www.choie.co/glossary/qa-testing), [Custom web application](https://www.choie.co/glossary/custom-web-application).

HTML: https://www.choie.co/glossary/ai-assisted-development