Intro to AI App Testing – “From Guesswork to Confidence” Webinar recording

Testing AI applications can feel unpredictable. You might run the same prompt twice and get two different, valid answers, which raises the question: how do you build reliable automated tests for that? AI app testing is a modern challenge.

If this challenge sounds familiar, this session is for you.

Moving beyond a simple pass/fail mindset is key to success. This webinar introduces real-world strategies for measuring the quality of AI-powered products. It’s a practical toolkit designed to help you get started.

In this session, you’ll learn how to:

Think in Layers: Learn to separate your application’s logic from the AI’s non-deterministic behavior. A testable design is a key strategy that allows you to test the scaffolding and plumbing around the AI with confidence.

Start with Sanity Checks: See how a simple keyword assertion can be an effective first step to identify critical issues early in the process.

Create “Golden Datasets”: You can’t measure quality without a benchmark. I’ll walk you through creating a “golden set” of ideal responses to serve as your source of truth for quality.

Build an “AI Judge”: Discover how to use a second LLM to automate quality checks. I’ll show you the prompting techniques that turn another AI into a reliable, scalable evaluator for your tests.

This session provides a practical framework to help you start testing your AI-based product with more confidence.

Ready to see these techniques in action? Check out the full recording.

Finding your way in the new world of AI testing? Get in touch to discuss how we can help.

Key Takeaways & Timestamps

This section breaks down the core concepts from the webinar. Use these timestamps to navigate to specific topics in the video.

• [03:25] The Core Challenge: Non-Deterministic AI
  • Unlike traditional code, AI models can produce different, valid answers to the same input, which makes traditional pass/fail testing ineffective.
• [05:33] A New Mindset: Aiming for “Good Enough”
  • Testing AI requires a shift from seeking a single “correct” answer to defining a range of acceptable, “good enough” responses.
• [06:47] How to Define “Good Enough”
  • Focus on invariants (consistent properties of a response) and boundaries (the acceptable range for a quality answer).
• [13:23] A Layered Testing Strategy
  • Separate your testing into two parts: the deterministic code you control (the “scaffolding”) and the unpredictable AI model. Thoroughly test your code to build confidence.
• [20:55] Testing Your Code (The Scaffolding)
  • Use unit and API tests to deterministically test your application’s logic by “mocking” (simulating) the AI’s response.
• [28:50] Testing the AI Model’s Response
  • Prompt the AI to return structured data (like JSON) to automate checks for schema, content types, and expected data.
• [31:46] Sanity Checking AI Content
  • Implement basic sanity tests to check for the presence of essential keywords or concepts in the AI’s output.
• [36:14] Using a “Golden Dataset” for Ranking
  • Create an ideal “golden” response to serve as a benchmark. Grade the AI’s actual responses against this template to measure quality.
• [39:01] Building an “AI Judge” for Automation
  • Use a second, independent AI model to evaluate and rank the primary model’s output, providing an objective quality benchmark.