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Traditional software quality assurance relies on predictable, deterministic behavior—the same input always produces the same output. But AI systems fundamentally break this assumption, creating a new challenge for quality assurance. This document outlines how to adapt QA practices for the AI era through effective evaluation and annotation system...

Every AI practitioner has faced this frustrating cycle: better models attract more users, whose interactions yield data to further improve the model. But how do you start this flywheel when you have zero user data? The solution lies in synthetic data generation - creating artificial yet realistic data to kickstart your AI systems before real us...

Your team was so excited to implement a new AI product feature. Finally, a chance for real-world application of OpenAI, Anthropic, and other AI providers. Your engineers dive right in, write up a prompt. Maybe implement RAG. Then you all stare in awe at the answers that are right most of the time… But something still isn’t right. “No one is ...

You’ve spent thousands on an AI observability platform. You’ve set up dozens of dashboards. And somehow, you still find yourself struggling to answer any question the CEO asks: Why did this customer get a hallucinated response yesterday? Why is this feature slower than it used to be? Why does this cost so much? After leading ML teams a...