QFireside Chat: The AI Eval Mindset

AI can produce an answer in seconds, sounding clear, confident, and useful; but sounding useful is not the same as being reliable. As software and quality engineering teams transition from traditional deterministic code to probabilistic AI, they can no longer rely on human-led “vibe checks” to verify quality. In this fireside chat, Millan Kaul will discuss how to build practical, automated evaluation pipelines that catch AI hallucinations and regressions before they reach your customers.

Key Takeaways:

1. Stop "vibe checking" your code
Ditch the 10,000-row benchmark dataset trap. Discover the 5-example "golden set" secret to catching critical AI failures instantly in your CI/CD pipeline.

2. Stop burning API tokens on basic formatting
Why pay an AI to check basic code? Discover the 3-Tier Assertion Stack that rejects bad AI outputs for free before they ever reach your expensive LLM judge.

3. Turn AI bugs into regression tests
Never let the same hallucination happen twice. Learn to capture live defects and turn them into automated CI/CD quality gates that block bad code before deployment.

4. Calibrate Your LLM-as-a-Judge
An uncalibrated LLM judge is just a subjective impression running on automated infrastructure. Treat your evaluator as a system under test. Learn how to calibrate your judge against human experts so false passes stop leaking defects into production.