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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.

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