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How We Test an AI Product Without Burning Credit
TL;DR: A team discusses testing an AI-powered product where each user action incurs model costs, and explains how they used Playwright to test an end-to-end chat flow inside The AI Platform. They highlight the challenge of validating a workflow that relies on multiple AI model calls and evaluation steps.
Testing an AI product is costly because user interactions trigger model calls. The team built a course inside The AI Platform, where learners chat with AI stakeholders, interview them, submit a memo, and receive an evaluator score. Each step involves real model calls, making end-to-end testing expensive and fragile. They explored using Playwright to automate the chat-driven flow while managing costs and reliability. The setup includes a multi-agent chat surface, a guide, stakeholder interviews, and a hidden AI evaluator that scores conversations.
Question for the room: What strategies have you used to balance coverage and cost when end-to-end testing AI-powered flows with real model calls?
— via dev.to
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