Ai Product Strategy

This distills frameworks from 94 product leaders into practical guidance for building AI products. Use it when you're deciding where AI fits in your product, evaluating build versus buy, or planning your roadmap. It pushes you to start with user problems instead of capabilities, design for model failures, and build feedback loops that improve over time. The concrete frameworks help with the messy middle decisions like defining human-AI boundaries and accounting for non-determinism. Especially useful if you're past the "should we use AI" question and into the harder "how do we actually ship this" territory.

skills.sh installs: 2.7K
GitHub stars: 1.5K
last updated: Aug 21, 2026

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Install

$ npx -y skills add refoundai/lenny-skills --skill ai-product-strategy --agent claude-code

Installs into .claude/skills of the current project.

Files

AI Product Strategy

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.

How to Help

Define the wedge - Identify high-friction chores where AI can provide a disproportionate payoff for the user.
Select the architecture - Choose between retrieval-augmented generation (RAG) and fine-tuning based on the need for live data vs. specific behavior.
Scale agency safely - Design a graduated approach to autonomy that keeps humans in the loop before moving to full automation.
Build for the curve - Align product roadmaps with future model capabilities rather than building complex scaffolding for today's limitations.

Core Principles

Account for squishy outputs

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."

SKILL.md on GitHub