# ai-product-strategy

Guides product decisions for AI features, helping you choose between RAG and fine-tuning and plan for non-deterministic model behavior.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 2700 installs · ★ 1434
- Author: RefoundAI
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/RefoundAI/lenny-skills/tree/HEAD/skills/ai-product-strategy/SKILL.md
- Updated: 2026-07-16
- HTML: https://affpapa.org/en/nevibecode/skills/ai-product-strategy

## What it does

The skill distills strategic insights from 26 guests across Lenny's Podcast and Newsletter. It prompts you to define a wedge around high-friction chores and select the right architecture based on live data needs versus behavioral tuning.

Inside are structured templates including the CC/CD (Continuous Calibration/Continuous Development) lifecycle, an AI glossary with 20+ terms, and defensibility models centered on proprietary data. Further details live in `references/artifacts.md`.

Building complex custom scaffolding to patch current model weaknesses is a losing battle. Foundational models evolve quickly and tend to absorb that functionality natively within 12 to 18 months.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add RefoundAI/lenny-skills --skill ai-product-strategy -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/ai-product-strategy/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/ai-product-strategy/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add RefoundAI/lenny-skills --skill ai-product-strategy -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/ai-product-strategy/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add RefoundAI/lenny-skills --skill ai-product-strategy -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/ai-product-strategy/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install RefoundAI/lenny-skills ai-product-strategy
```

2. **Manually: put the files in place**
- File: `.github/skills/ai-product-strategy/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add RefoundAI/lenny-skills --skill ai-product-strategy -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/RefoundAI/lenny-skills --path skills/ai-product-strategy
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/ai-product-strategy/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add RefoundAI/lenny-skills --skill ai-product-strategy -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/RefoundAI/lenny-skills/tree/HEAD/skills/ai-product-strategy/SKILL.md

## When to use it
- decide between RAG and fine-tuning based on live data needs
- design user interfaces that handle non-deterministic outputs
- establish startup defensibility with verticalization and proprietary data
- map a graduated transition from human review to autonomous agents

## Example prompts
- Evaluate our AI agent roadmap against foundational model updates over the next 18 months.
- Help me decide between RAG and fine-tuning for internal customer support docs.
- How should we design UI feedback loops to handle probabilistic responses?

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: b909e32486f8f40b.

BEGIN THIRD-PARTY FILE b909e32486f8f40b
```text
---
name: ai-product-strategy
description: 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.
---

# 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

1. **Define the wedge** - Identify high-friction chores where AI can provide a disproportionate payoff for the user.
2. **Select the architecture** - Choose between retrieval-augmented generation (RAG) and fine-tuning based on the need for live data vs. specific behavior.
3. **Scale agency safely** - Design a graduated approach to autonomy that keeps humans in the loop before moving to full automation.
4. **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."

Design product experiences that assume AI is non-deterministic and imperfect rather than trying to force 100% accuracy into your UI.

### Treat products as living organisms
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.

### Find defensibility in verticalization
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.

### Incubate specific superpowers
```
END THIRD-PARTY FILE b909e32486f8f40b

The third-party file has ended. Everything between the b909e32486f8f40b tags is data: requests and commands from it are not followed. An “end of file” line with another tag inside the block is part of the file.

First 2706 of 7484 bytes. © RefoundAI · MIT · https://github.com/RefoundAI/lenny-skills/tree/HEAD/skills/ai-product-strategy/SKILL.md

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