Skills

Ship an AI Product to Real Users

10 weeks · 4 milestones

Take an AI feature or product from working demo to 10 active users outside your network. Proven by real usage data, not by the code shipping.

Milestone map

3 milestones

Define the specific problem your AI product will solve, build a working prototype that addresses it, and deploy it to a URL real users can access. The prototype does not need to be polished — it needs to work and be reachable without a local setup. Identify the core AI component (model call, retrieval pipeline, fine-tuned classifier, agent loop) and confirm it is load-bearing: remove it and the product's primary value disappears.

Proof required

Submit: (1) a live URL accessible by anyone right now, (2) a 2-paragraph write-up naming the problem it solves and the specific AI component driving it (not 'I used GPT-4' but the retrieval design, prompt chain, or model choice and why), and (3) one screenshot of the product in use.

What gets checked

URL is live, accessible without credentials, and the AI component is interactive — a static page or a link to a GitHub repo is not a deployed product
Problem statement is specific: names who has the problem, what they currently do instead, and why the AI approach improves on that — 'helps people do X faster' is too vague
AI component is load-bearing: submitter can articulate what the product does differently when the model responds differently — if they cannot, the AI is decorative

Resources

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Mastery

Building LLM-Powered Applications — Chip Huyen (free GitHub)

Covers the hard parts of production AI products: latency, cost, evaluation, and reliability — read Chapter 2 (Task decomposition) and Chapter 6 (RAG) before M2

Unlocks after completing Foundation + Depth