How AI assists users
Across the Gabaltech Platform, AI helps generate infrastructure code, review pull requests, suggest deployment configurations, and answer questions about your own environment. In every case, it's producing a suggestion for a person to review โ not an unattended action.
AI never replaces human judgement
AI doesn't decide what should be built, whether a change is safe, or whether a deployment should go ahead. Those remain decisions made by the engineers using the platform, informed by AI output rather than dictated by it.
Recommendations should be reviewed
Treat AI-generated Terraform, Pulumi, Kubernetes manifests or application code the same way you'd treat a first draft from a colleague: read it, understand it, test it, before you rely on it. We surface AI-generated content clearly so this review step is obvious, not optional.
Limitations of AI
Language models can be confidently wrong. They can miss edge cases, misunderstand context, or generate code that looks correct but isn't. We design our workflows assuming this will happen sometimes โ automated validation, testing, and human review exist specifically to catch it.
Bias reduction
We rely on established, well-audited foundation models rather than training our own from scratch, and we scope AI features to technical tasks (code, infrastructure, documentation) where the risk of biased or harmful output is far lower than in open-ended decision-making about people.
Continuous improvement
We track where AI-generated output gets rejected, corrected, or rolled back, and use that as a feedback signal to improve prompts, guardrails and validation โ not just to improve the model.