duo-ai-product-strategy
Decide where to point AI so it changes your cost structure instead of adding a feature — which bottleneck absorbs the automation budget, how to divide labor between expert staff and models, when to adopt a capability that is still visibly bad, and how to make AI-produced output trustworthy enough for a skeptical or regulated buyer. Use when someone asks "should we build this with AI at all", "what is our AI strategy", "where do we actually apply AI", "will this be a moat or just a feature", "how do we automate content production without wrecking quality", "which work stays human", "is it too early to adopt this", "how do we get enterprise or regulated customers to accept AI output", or "what is the real ML problem behind this bottleneck". Distilled from Duolingo's public posts on the English Test, LLM-assisted content, and expert-plus-AI pipelines.
- Version
- 2.0.0
- License
- MIT
Pinned to revision 78072c9528fb, so it is the text this page describes rather than whatever the author pushed since.
Files
- skills/duo-ai-product-strategy/SKILL.md
- skills/duo-ai-product-strategy/references/adopt-early-gate-on-your-own-quality-bar.md
- skills/duo-ai-product-strategy/references/attack-the-incumbents-physical-bottleneck.md
- skills/duo-ai-product-strategy/references/author-a-pool-deliver-an-instance.md
- skills/duo-ai-product-strategy/references/name-the-real-ml-problem-first.md
- skills/duo-ai-product-strategy/references/point-ai-at-unit-economics-not-features.md
- skills/duo-ai-product-strategy/references/simplify-until-automatable-name-what-must-survive.md
- skills/duo-ai-product-strategy/references/spend-the-automation-budget-on-the-gate.md
- skills/duo-ai-product-strategy/references/stage-split-humans-set-constraints-models-multiply.md
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