WeTest.Athens 2026

Did you ever wish you could ask ChatGPT about your product requirements, past test cases, or technical documentation? Join this hands-on session to learn how to build an AI using Retrieval-Augmented Generation that understands your product or organization’s context. I’ll guide you step by step, from the theory to the actual development, so you can customise your own AI helper for your daily work.

AI has transformed how we write code, but not always for the better. How do you ensure that what you, or your AI assistant, are creating is actually scalable and future proof?

Many teams rush to adopt tools like GitHub Copilot and ChatGPT for test automation, only to discover later that their codebase has become unscalable, duplicated, and inconsistent. The real challenge isn’t generating tests faster. It’s keeping what’s built reliable, maintainable, and ready for growth.

For this session, we gathered insights from industry experts and combined them with our own real world experiences to define what makes a test automation framework maintainable and scalable. We’ll show how to identify and avoid architectural pitfalls that silently grow into maintenance nightmares and break down what defines a scalable framework in practice. For practical examples we will use Playwright and TypeScript.

Expect patterns, anti‑patterns, and lessons learned from modern tech environments that help you design systems able to evolve instead of collapse under their own speed. Whether you’re starting a brand new project or improving your current setup, this talk will help you to build a framework that grows with your needs, not against them.