Generative AI Meetup Dissects Evals, Speech

The strongest takeaway is that dependable AI comes from measurement and engineering feedback loops, not increasingly elaborate prompts.

• –LLM judges need validation against human-labeled examples and ongoing drift monitoring
• –Trace-free evals can make quality testing more portable, lightweight, and accessible
• –Spec-driven engineering turns ambiguous AI behavior into explicit, testable requirements
• –TPU-scale speech-to-speech systems highlight latency, throughput, and infrastructure tradeoffs
• –The topics together frame evals as core product infrastructure rather than a post-launch checklist