the only way to quickly ramp on hard skills is to get hands on: not just building things but *thinking about how you would get a team to build that thing.* What are the "requirements"? What would you measure? How do you share specs for a non-deterministic experience? How do you design for trust?
"How would you ensure this will be a high quality experience across the breadth of use cases you're describing" and often, the response is punted to engineers -- "oh, I'll set the goal and trust engineers can figure it out." Or worse--"oh, no one minds a few hallucinations."
the reality is, just like with other products, a PM has to be able to articulate sufficient detail in goals, outcomes, and user experience to match the execution of the product with the problem they're solving. For gen AI applications, this is going to include: - prompt strategy, writing, and testing - scoping agent tasks - retrieval strategies - context management - feedback strategies - unit economics - security & prompt injection hardening - model selection - identifying and structuring sources for fine tuning - chains & agent orchestration - analytics & quality management
It's super early, and I believe that product leaders that invest now in what I'll call "hard AI product skills" are going to be way ahead of the game for when these capabilities inevitably become table stakes for software companies.
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