What does vibe coding mean?
In a vibe-coding workflow, you explain a goal in ordinary language, ask an AI tool to generate or change the software, run the result, and describe what should happen next. The conversation becomes a major part of how you build. You might ask for a project dashboard, inspect it in a browser, and then request a saved filter or a different way to handle an error.
The term became popular after Andrej Karpathy used it in 2025. Google Cloud distinguishes an exploratory version that places substantial trust in generated output from a more responsible AI-assisted workflow where the person reviews and tests the code. That distinction matters: creating something that looks right is different from understanding whether it works for the people and data it will handle.
Vibe coding, AI-assisted coding, and no-code: what changes?
These labels overlap, and products often combine them. The useful question is what you control and what you can inspect. A visual builder may expose a workflow canvas; an AI app builder may generate a repository; an editor assistant may work inside code you already maintain. Compare the actual output, data ownership, and maintenance path.
For a weekend experiment, you may accept an output you cannot yet explain. For a service that stores customer work, identify who will investigate a permission failure, restore data, or review an unexpected change. The amount of typing you do does not settle those responsibilities.
| Approach | Typical way to make a change | What to inspect |
|---|---|---|
| Conversational app building | Describe a behavior and ask the AI to revise the app | Generated behavior, code changes, data access, and deployment |
| AI-assisted coding | Direct an assistant within an engineering workflow | The proposed diff, tests, dependencies, and integration with existing code |
| Visual no-code building | Configure screens, data, and workflows through an interface | Platform capabilities, permissions, export options, and service limits |
Which tools can you use for vibe coding?
Start by deciding whether you need an app builder that packages a project and publishing workflow, or an assistant inside a development environment you already use. Lovable, Bolt, and Replit are three candidates for the web-app-building route. Their documented workflows differ, so use the AI app-builder comparison to decide what to try first.
Check the result you can take away: a repository, saved database records, a deployment, and a clear account for the services running behind it. A generated interface is only one piece. If your requirement is an app-store release, offline use, a particular server runtime, or background processing, verify that requirement with the candidate before starting.
A worked example: plan a tiny project-brief app
Consider a fictional app called BriefBoard. A freelancer creates a brief with a title, project goal, and deadline, then returns later to edit it. This is an illustrative exercise, not a claim that we built or tested this app in a particular vendor’s platform. The downloadable brief gives you a small, repeatable task to try yourself.
Make the first version narrow: one owner, a list of briefs, an editor, and a saved result. Leave payments, public sharing, file uploads, and AI-generated advice out of this first exercise. Each additional capability introduces more states to explain and verify. The MVP guide helps choose a useful boundary around the first task.
Build a small web app for a freelancer to save project briefs. Each brief has a title, project goal, and deadline. Show a list, a create form, and an edit form. Save records so they survive a reload. Reject an empty title with a helpful message. Show empty, saving, success, and failure states. Begin with synthetic demonstration data. Explain where data is stored and which services the project requires. Do not connect payments, send emails, or publish the app yet. Before adding real users, explain the authentication and server-side ownership checks needed so one account cannot read or change another account’s briefs.
Iterate by describing the failure and the expected result
After the first output, complete the task as a user would. Create a brief, reload, reopen it, and edit it. If the record disappears, say which action you took and what happened. Ask the tool to inspect the storage path and explain the proposed fix. A precise report gives the next change a testable purpose.
Keep changes small enough to review. Ask for a deadline validation change separately from a redesigned dashboard. Save a known working version before a larger change, inspect what changed, and repeat the previous acceptance checks. A later prompt can affect behavior that already worked.
I created a brief, saw it in the list, and reloaded the page. The list became empty. I expected the saved brief to remain available. Inspect the save and load paths. Explain the cause before changing them. Fix persistence without changing the form fields or adding new services. Then show how I can verify create, reload, edit, and reload again.
Check the workflow before adding real customers
Turn the intended result into explicit checks. Browser tests can confirm that a button completes a workflow, but a permission rule also needs testing at the server or data layer. Use two separate test accounts and permitted synthetic records. Hiding another account’s record in the interface does not establish that a direct request is blocked.
Have someone with the relevant engineering experience review the parts you cannot evaluate confidently. Give them the actual repository, configuration, and acceptance criteria. Keep secrets on the server, identify the deployment and database owners, and confirm a recovery procedure. Treat a generated security explanation as something to verify against the implementation.
| Check | Expected evidence |
|---|---|
| Save and reload | The same record returns from the intended storage |
| Invalid input | An empty title is rejected without losing other input |
| Two accounts | The server rejects cross-account reads and writes |
| Failed save | The interface reports failure and preserves the draft |
| Follow-up change | Previously working create and edit flows still work |
| Recovery | A documented restore or rollback procedure is checked in the intended environment |
What are the benefits and limits of vibe coding?
A conversational workflow can make it easier to express an idea and explore a small interface without first writing every line yourself. It can also give an experienced developer a starting point for a bounded change. How much time it saves depends on the task, the generated result, the review needed, and how well the next change fits the project.
Common problems include unclear requirements, a convincing interface with incomplete storage, hidden service dependencies, and changes that introduce new failures. Track the work needed to reach a reliable result, including corrections and handoff. Avoid treating a fast first screen as evidence of a finished product or a sustainable business.
Can you build and run a SaaS this way?
AI-assisted tools can participate in building a SaaS, but the service still needs a useful customer task, reliable behavior, access control, support, and an operating budget. Separate the cost of building with an agent from the cost of hosting the finished app and any AI calls your customers make. The AI cost-control guide explains that second layer.
Once the first workflow is checked, invite a small relevant group to try it, observe where they get stuck, and improve the actual product. The first-user marketing plan covers that next step. Use the downloadable exercise to define what you are building and what would count as working before increasing the scope.