AI-assisted features are built to the same engineering bar as the rest of the Gabaltech Platform โ the fact that a model is involved doesn't lower the standard.
Prompt versioning
Prompts and system instructions that drive AI features are version-controlled alongside the code that uses them, so changes are reviewable and reversible.
Model versioning
We pin model versions rather than floating on "latest", so behaviour doesn't shift under us without a deliberate, tested upgrade.
Testing
AI-assisted code paths are covered by the same unit and integration test expectations as any other feature.
Regression testing
When we change a prompt or upgrade a model, we re-run known good/bad cases to catch regressions before rollout.
Infrastructure validation
AI-generated infrastructure goes through terraform validate / pulumi preview โ syntactic and semantic validation โ before it's ever eligible for apply.
Terraform validation
Our Terraform module catalog includes terraform test coverage; AI-generated Terragrunt units consuming those modules inherit that validation.
Kubernetes validation
Where AI generates Kubernetes manifests, they're validated against schema (and, where configured, policy-as-code) before apply.
Policy-as-Code
We're building toward automated policy checks (tagging standards, region lockdown, cost guardrails) that apply equally to AI-generated and human-written infrastructure.
CI/CD testing
AI-assisted changes flow through the same GitLab CI pipelines โ build, test, plan, gated apply โ as everything else. There is no separate, lighter-weight path for AI-authored changes.
Monitoring & observability
AI-assisted deployments are instrumented the same way as any other service โ logs, metrics, traces โ so their behaviour in production is as visible as anything else running on the platform.