Code Quality in the AI Era

We can write code faster than ever, but how do we ensure it's good enough to be relied upon? From test coverage to security to sustainable review practices, this workshop tackles quality head-on.

Who This Is For

Engineering leads responsible for code quality and security standards
Senior developers reviewing AI-generated code from their teams
CTOs and VPs of Engineering navigating AI adoption policies

Agenda

Morning

8:30Setup & introductions

9:00Why AI-generated code passes its own quality checks

10:00Security scanning workflows for AI code

11:00Catching real vulnerabilities hands-on

Afternoon

12:00Lunch

1:00Sustainable code review practices at scale

2:00Building a team AI code generation policy

3:00Quality checklist and review workflow design

Learning Goals

Key Concepts

Why AI-generated code passes its own quality checks — and why that is dangerous
Test coverage strategies when code volume outpaces human review
Security risks unique to AI-generated code: secrets exposure, injection, and trust boundaries
Sustainable code review practices at scale
Interface contracts, refactoring, and when reuse still matters

What You'll Take Away

A practical quality checklist for AI-generated code
Automated security scanning workflows you can deploy immediately
Strategies for reviewing code written by non-engineers
A team policy template for AI code generation
Hands-on experience catching real vulnerabilities in AI-written code

In the Workshop

Instructor

Jeff Casimir has spent two decades building technical education programs, including founding the Turing School of Software & Design. He brings deep experience in hands-on, project-based learning to every session.

What You Need

A laptop running macOS or Windows (tablets are not sufficient)
A modern web browser (Chrome or Firefox recommended)
Claude access will be provided — setup instructions sent before the workshop