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Ship a Real AI Product: From Problem to Real Users

Advanced · ship a real product · 18 lessons · ~8 hours

Ship a Real AI Product: From Problem to Real Users

The advanced builder path. Pick a real problem people actually have, cut it to the smallest useful version, engineer the LLM parts with quality bars and cost budgets, deploy behind a real URL, and put it in front of real users. Five project lessons, five real artifacts.

What you walk away with

Syllabus

1. Start from a real problem, not an idea

A common failure pattern: building a solution before confirming the problem. Learn to find a problem that is real.

  • · Why most AI projects die
  • · Find a painful, frequent, reachable problem
  • · Project: Talk to 3 real people, write the problem note

2. The smallest useful version

Cut scope until it hurts, then test demand before you build the real thing.

  • · Cut scope until it hurts
  • · Fake-door and concierge tests before writing code
  • · Project: Define your v1 in one page

3. LLM engineering for production

Keys and costs, prompts as artifacts, a measured quality bar, and retrieval only when it earns its place.

  • · API keys, cost meters and rate limits
  • · Prompts as versioned engineering artifacts
  • · Evals: 20 hand-graded examples beat vibes
  • · RAG: when retrieval helps and when it is dead weight
  • · Project: Working prototype with a measured quality bar

4. Survive real usage

Failure modes, guardrails, cost budgets, and a real deployment with logging.

  • · Failure modes: hallucination, jailbreaks, silent wrongness
  • · Guardrails and graceful fallbacks
  • · Cost and latency budgets: measure, cache, degrade
  • · Project: Deploy behind a real URL with logging

5. Real users, honest iteration

First users without ads, feedback you can trust, and the shipped v1 retro.

  • · Get the first 10 users without ads
  • · Read feedback without fooling yourself
  • · Project: Ship the v1, log the build, write the retro
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Ship a Real AI Product: From Problem to Real Users | Aryabhatt AI