in this conversation, you’ll learn:
why ai demos feel magical but real product usage feels exhausting.
what ai evals actually are and why they are becoming essential to shipping ai products.
how reliability, not intelligence, determines whether users trust ai.
what product managers must build around models to make them usable in the real world.
where to find prayerson:
in this episode, we cover:
(0:00 - 2:30) the ai magic show
why polished demos create unrealistic expectations about ai capabilities.
how the first experience with a tool feels fundamentally different from daily usage.
(2:30 - 5:30) the reality check
what happens when you try to use ai for real work.
why users end up double checking, rewriting, and correcting outputs.
(5:30 - 8:30) the hidden problem
why the issue is not simply model intelligence.
what gap exists between model performance and product reliability.
(8:30 - 12:00) understanding ai evals
what “evaluation” means in ai systems compared to traditional software testing.
why variable outputs change how quality must be measured.
(12:00 - 15:30) shipping ai safely
how teams monitor model behavior after launch.
why guardrails matter more than prompts.
(15:30 - 19:00) the new job of the product manager
how product managers move from feature planning to system design.
what responsibilities emerge when you ship probabilistic software.
(19:00 - 22:30) trust as a product feature
how reliability shapes user adoption and retention.
why consistent behavior matters more than impressive responses.
(22:30 - 26:00) building feedback loops
how real usage data improves ai products over time.
why continuous measurement becomes part of the product itself.
(26:00 - 29:30) from tools to systems
how ai products differ from traditional saas applications.
why orchestration, monitoring, and evaluation become core infrastructure.
(29:30 - 33:00) the future of ai products
how companies that operationalize evaluation gain an advantage.
what separates experimental ai apps from dependable platforms.
