"Vibe coding" — the practice of generating code with AI prompts and shipping it based on the feeling that it works — has become a legitimate development style. It's fast, low-friction, and democratizing. It also produces code that frequently has reliability problems hiding just below the surface.
Antigravity's Artifacts feature offers a practical path out of this problem. This guide explains what makes vibe coding unreliable, what Artifacts are, and how to run a verification loop that keeps the speed benefits while closing the quality gap.
What the Reliability Problem Actually Is
AI-generated code usually works on the happy path. The problem is everything else: what happens when the input is empty, the network fails, the user does something unexpected, or two components interact in a way the prompt didn't anticipate.
This class of problem is hard to notice because:
The code runs without errors during your testing, which is usually limited to expected inputs. The failures are silent — wrong data written to a database, a promise that never resolves, a permission check that always returns true.
The gap between "it runs" and "it handles all the cases that matter" is exactly where vibe coding tends to fall short. This is manageable for personal prototypes. It's a real problem for anything other people will use or that touches sensitive data.
What Antigravity Artifacts Are
Artifacts in Antigravity are distinct, versioned units of generated code. Instead of code appearing inline in a chat response (and getting buried), it's saved as a separable artifact with its own lifecycle.
This separation unlocks a few things that matter for verification:
You can target a specific artifact for follow-up instructions: "review this artifact for security issues" or "write tests for this artifact" — without the context of the entire conversation getting in the way.
You get a clear history of changes. When an artifact is revised, you can see what changed and why. This is useful when debugging: was the problem introduced in revision 2 or revision 3?
You can run artifacts in an isolated environment before integrating them into the larger project. Problems surface in isolation rather than tangled with other code.
The Four-Step Verification Loop
This workflow maintains most of the speed benefit of vibe coding while adding meaningful reliability checks.
Step 1: Generate and save as an Artifact
Generate code in Antigravity as you normally would. When Antigravity produces the artifact, save it before moving on. Don't integrate it immediately.
Step 2: Request a self-review
Ask Antigravity to review the artifact it just generated. A prompt like this works well:
"Review this artifact for: missing error handling, edge cases that could cause failures, and any security concerns."
AI can review its own code with reasonable objectivity. It will typically identify issues like missing null checks, unhandled promise rejections, unsanitized inputs, or logic that breaks on boundary values. These are exactly the things vibe coding tends to miss.
Step 3: Fix and re-review
Take the issues from Step 2 and ask Antigravity to address them: "Update the artifact to fix the issues from the review."
After the revision, repeat Step 2. Run the loop until the review comes back clean. In practice, two or three rounds handles the vast majority of reliability issues.
Step 4: Generate test coverage
Once the artifact is stable, ask for tests: "Write unit tests for this artifact covering both expected behavior and failure cases."
Save the tests as a separate artifact and run them. If they don't pass, something in the implementation or the tests needs to change — either way, you've found a real problem before it reaches production.
Calibrating Verification to Risk
Applying the full four-step loop to every piece of code isn't practical. Matching verification depth to risk level makes the process sustainable.
High risk — authentication, payment processing, personal data handling: run all four steps, and consider a human review of the final artifact. Failures here are expensive.
Medium risk — database reads/writes, external API calls, business logic: run Steps 2 and 3 (self-review plus fixes). The self-review catches most of what matters.
Low risk — UI components, styling, static content, utility functions: Step 1 is usually sufficient. If something breaks, it's visible and easy to fix.
This calibration prevents the verification process from becoming a bottleneck while focusing effort where it actually reduces risk.
What Vibe Coding Is Actually Good For
Vibe coding isn't going away, and the right response isn't to abandon it — it's to use it appropriately.
For prototyping and ideation, it's genuinely excellent. Getting from concept to a working demonstration in hours rather than days has real value — it lets you validate ideas before investing in production-quality implementation. The problem is when prototype-quality code gets promoted to production without the verification work that should accompany that transition.
The Artifacts verification loop is designed to fit into that transition. It doesn't require deep code review expertise from the developer — the AI does the review. It doesn't require extensive test writing by hand — the AI generates the tests. What it requires is the discipline to run the loop before shipping.
Starting Point
If the full workflow feels like too much at once, start with just Step 2 on your next piece of generated code. Ask Antigravity to review the artifact it just wrote. See what it identifies.
Most developers who try this are surprised by how consistently AI self-review catches things they would have missed — and by how straightforward it is to fix them. That first experience tends to make the habit stick.