CI/CD (Continuous Integration and Continuous Delivery)

What is CI/CD?

CI/CD stands for Continuous Integration and Continuous Delivery, two linked practices that automate how code moves from a developer's machine to users. Continuous integration means developers merge their changes into a shared codebase often, and each merge triggers an automated build and test run. Continuous delivery means every change that passes those checks is packaged and ready to release, often to a staging environment first. Continuous deployment goes one step further and releases passing changes to production automatically.

In practice, a pipeline runs on a service such as GitHub Actions or GitLab CI. When code is pushed, it installs dependencies, runs tests and security checks, builds the application and deploys it to the target environment.

CI/CD shows up in any team that ships software regularly, from SaaS products to internal tools and data pipelines. It turns releases from rare, risky events into routine ones. Without it, releases depend on manual steps that are easy to skip under pressure.

Why does CI/CD matter for a business?

It makes change cheaper and safer. Small, frequent releases are easier to test, review and roll back than large ones, so problems are found sooner and fixed faster. Customers get improvements continuously instead of waiting for a big release.

It also reduces dependence on individuals. When building and deploying are automated, a release does not require the one person who knows the manual steps, and every release goes through the same checks.

For leadership, CI/CD makes delivery visible. The pipeline records what changed, when, and whether it passed. That history helps with audits, incident reviews and compliance questions, and gives a clear picture of how quickly the team can respond to customers and fix problems when they appear.

What does a typical CI/CD pipeline include?

Pipelines differ between teams, but most share the same core stages, run in order on every change.

• Source control: every change goes through a shared repository, usually with pull requests and review.
• Automated build: the application is compiled or bundled the same way every time.
• Automated tests: unit, integration and end-to-end tests run on each change.
• Quality and security checks: linting, dependency scanning and secret detection.
• Deployment stages: preview or staging environments first, then production, with an easy rollback.

Teams add steps as they mature, such as database migration checks, performance tests or approval gates for regulated changes. The pipeline is code too, and should be reviewed and maintained like the product. A broken pipeline deserves the same urgency as a broken product.

How does CI/CD apply to AI-generated code and AI features?

As more code is written with AI tools, automated checks matter more. A pipeline applies the same tests and security scans to generated code as to code written by people, which catches many problems a quick glance would miss. That makes it an essential safeguard for teams using vibe coding or other AI-assisted methods.

AI features need extra checks of their own. Changing a prompt or a model can alter outputs without any code changing. Teams add AI evaluations (evals) to the pipeline: a set of test inputs with expected qualities, run automatically before release, so drops in accuracy or changes in tone are caught before users see them. Results from each run are kept, so the team can see how quality moves over time.

How do you get started with CI/CD?

Start small and automate the most painful step first. For many teams, that is running the existing tests on every pull request. Next, automate the build and a deployment to a staging or preview environment, then production deployment with a simple rollback.

If there are few or no tests, add them around the most important workflows before relying on automation. A pipeline that passes everything because nothing is tested gives false confidence.

Keep the pipeline fast. If checks take too long, developers batch changes and the benefit of small releases disappears. Over time, a reliable pipeline also makes it practical to pay down technical debt, because each cleanup can be shipped and verified on its own.

How does Vizio AI set up CI/CD?

CI/CD is part of how our Venture Studio handles software development on the products we build. During MVP development we set up source control, automated tests and preview deployments from the start, so the team can ship small changes safely. For SaaS platform architecture and AI-enabled product features, we add the checks those products need, including evaluation runs for AI behavior.

For existing products, setting up or repairing the delivery pipeline is often an early step in product modernization. The starting point is a review of how code currently gets from a developer to users.