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Help users decide where to apply AI effectively, manage the transition from deterministic to probabilistic software, and build long-term defensibility through verticalization and proprietary data.
Passed 21 of 22 test cases with the skill, and 19 of 22 without it.
3 fixed18 passed either way0 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 3 test cases the skill fixed. Same model, same prompt, with and without the skill.
Our lead architect wants to allocate two quarters of engineering time to build a complex custom context-chunking and multi-agent routing framework to work around current LLM context window limits and reasoning failures. How should we evaluate this roadmap decision?
| Checked | Without skill | With skill |
|---|---|---|
| Advise against building heavy custom scaffolding designed around current model limits. | Passed | Passed |
| Recommend designing the product roadmap for capabilities expected from upcoming base models in 12 to 18 months. | Missed | Passed |
The instructions your model reads when the skill loads.
Prioritize high-impact workflows and navigate non-deterministic development to build defensible AI products.
Help the user with ai product strategy using insights from 26 guests and posts across Lenny's Podcast and Newsletter.
Alex Komoroske: "LLMs allow writing shitty software to be significantly cheaper, not necessarily good software, but good enough in certain contexts. And also it means that there's certain software now that isn't plain old computing that can be run cheaply. It's relatively expensive marginal cost."
Design product experiences that assume AI is non-deterministic and imperfect rather than trying to force 100% accuracy into your UI.
Asha Sharma: "Because these models are so effective at this point, you want to start to tune them to certain types of outcomes. All of a sudden, these are these living organisms that just get better with the more interactions that happen. I think this is the new IP of every single company products that think and live and learn."
Measure success by the team's metabolism in ingesting data and improving learning loops rather than static feature releases.
Logan Kilpatrick: "We're not going to launch some of these varied verticalized products. We're not going to launch an AI sales agent. That's just not what we're building towards. And companies who are and have some domain specific knowledge and they're really excited about that problem space, they can go into that and leverage our models and end up continuing to be on the cutting edge without having to do all that R&D effort themselves."
Avoid competing with foundational models by targeting specific industry niches where domain expertise provides a structural advantage.
Noah Weiss: "I think in the AI space, we're trying to hear from customers, what do you wish Slack could do if it had these new superpowers? Let's incubate a couple teams or prototype, give them space to run and pilot and then get something to launch that's amazing. Blows people away. That's the formula that we've seen."
Avoid generic AI features by identifying specific customer needs and giving dedicated teams space to prototype them independently.
Sherwin Wu V2: "The field and the models themselves are just changing so, so quickly. They tend to disrupt themselves. The models will eat your scaffolding for breakfast."
Design for the capabilities expected in 12 to 18 months to avoid building custom scaffolding that will eventually be absorbed natively by the models.
test cases passed, vs 19 / 22 without
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