From AI Prototype to Production with Ankur Goyal

EPISODE 27

Ankur Goyal joins Founder Mode to show how real teams get from AI prototype to production: build a two-click loop from user complaint to eval, treat observability as a driver of quality, and design iteration environments that connect production logs back to tests. Ankur explains why LLMs behave more like databases than CPUs, how to avoid eval fatigue by curating the 5–10 examples that matter, and why top teams re-evaluate model choices monthly. He also looks ahead to agents that can review and improve other models’ work, turning today’s manual feedback loops into scalable systems.

CHAPTERS

07:53 – Why prototypes break in production

10:22 – Iteration environments and closing the loop

12:21 – LLMs are databases, not CPUs

14:48 – Beating eval fatigue with ruthless prioritization

21:15 – Observability as a driver of quality, not uptime

25:25 – What’s next for evals, agents, and AI infra

LINKS

Connect with Ankur Goyal

usebraintrust.com • LinkedIn • X/Twitter

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