Vibe Coding: How AI IDEs Are Changing Software Development
“Build a dashboard that shows unpaid invoices and lets me filter by customer.”
A request like that can now start a software project. An AI coding tool may generate screens, connect components, and help fix errors through conversation.
For someone who has spent years explaining an idea without being able to demonstrate it, that is a meaningful change.
Vibe coding generally describes building software by prompting AI and iterating on the result, sometimes without closely examining the generated code. AI-assisted development is broader and can include rigorous code review, testing, and deliberate engineering decisions.
The distinction matters once a prototype starts handling real business information.
What an AI IDE changes
An integrated development environment, or IDE, is the workspace used to build software. AI features can help interpret a codebase, propose changes, and assist with tasks that would otherwise require considerable manual work.
Cursor, for example, documents agent capabilities as part of its development environment. Exact features and permissions depend on the tool and configuration.
The practical appeal is straightforward: people can test ideas sooner, and developers can spend less time on some repetitive tasks.
A working screen is an early milestone
Suppose you create an invoice dashboard for a small agency. It looks convincing. Filters work. The totals appear correct.
Then another employee signs in.
Can they see invoices belonging to a different company? What happens when the same payment notification arrives twice? Does the total include cancelled invoices?
These are ordinary software questions. They do not disappear because the first version was generated quickly.
AI can also produce plausible explanations for flawed code, so asking the same tool whether its work is secure cannot be your only review process.
Where vibe coding can be useful
It can be a good way to explore layouts, build disposable demonstrations, or test a narrowly defined idea using synthetic data.
The risk changes when the application handles customer records, payments, credentials, or essential operations. At that point, someone needs to understand and own the implementation.
GitHub’s guidance on reviewing AI-generated code emphasises functional checks, testing, and human oversight. Generated output still needs verification.
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Build quickly, then check deliberately
A practical process is to:
- Describe expected behaviour and limits before generating code.
- Make small changes that can be inspected.
- Keep version history so changes can be reversed.
- Test failures, permissions, and unusual inputs.
- Review dependencies and protect secrets.
- Arrange competent review before a sensitive production launch.
Ask the tool to explain unfamiliar decisions, but verify those explanations against the code and its behaviour.
AI IDEs lower the effort required to start building. That is valuable. The harder question is whether the resulting application remains understandable when something breaks six months later.
A useful prototype proves an idea can be demonstrated. Reliable software proves it can keep working under less convenient conditions.
