AI-assisted coding is no longer a future trend—it’s part of the modern developer workflow. Tools can generate functions, classes, and even entire modules in seconds. But raw code generation is only half the story. Without the right engineering practices, teams risk shipping untested, fragile, or insecure code to production.

This session takes a practical look at using AI effectively in day-to-day development. We’ll start with prompt strategies for “vibe coding”—techniques for getting high-quality, context-aware output from AI. From there, we’ll walk through the guardrails required before merging AI-generated code:

Linting & static analysis to catch common errors and enforce consistency

Unit and integration testing to validate correctness

Code reviews focused on maintainability, edge cases, and architectural fit

CI/CD automation to integrate these checks into the pipeline

By combining fast iteration with disciplined engineering checks, developers can safely leverage AI to boost productivity without compromising reliability. Attendees will walk away with practical workflows and a clear checklist for making AI-assisted code production-ready.

AI can generate code quickly, but shipping production-ready code still requires rigor. This talk covers prompt strategies for “vibe coding” with AI and the technical checks—linting, testing, static analysis, and reviews—that ensure code quality before deployment.