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:
* x: https://x.com/iamprayerson
* linkedin: https://www.linkedin.com/in/prayersonchristian/
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.
be part of the conversation at iamprayerson. subscribe at no cost to get new posts and episodes delivered to you.
