Designing and Building AI Products

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Hey, it’s Kushagra. Welcome to this week’s AtlasMoth drop.

This one’s personal. I have just completed MIT xPRO’s Designing and Building AI Products and Services course.

The biggest win? Learning a structured way to turn raw AI tech into products people actually trust.

My capstone? A reimagined dating app. Not swipes. Not “ideal profiles.” But real chemistry is measured through conversation.

This is how the MIT frameworks structured the solution.

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1. The Burnout Problem

Modern dating apps leave users drained.
A Forbes Health survey found that 78% of respondents feel emotionally fatigued.

Why? The swipe game is shallow. People scroll through curated photos and bio snippets. But spark doesn’t live in a static profile; it lives in conversation.

That’s where the reframe begins.

2. From Profiles ➝ Conversations

The play: use a fine-tuned LLM to analyze live chats.

Instead of just looks or “likes,” the AI reads tone, humor, and flow, spotting when two people actually click.

This shift from what people say they are → how they actually connect attacks burnout at its core.

3. The Tech Stack

The system leaned on MIT’s four-step AI design process (Prof. Brian Subirana):

• Intelligence: Predict conversational chemistry.

• Business Process: Flywheel strategy (more chats → smarter matches → more chats).

• AI Tech: Embeddings + vector DBs + fine-tuned LLM with RAG for explainability.

• Tinkering: Stress-test bias, hallucinations, adversarial inputs, cost, and drift.

All underpinned by user consent + data ethics.

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4. Designing for Trust

If the AI is a black box, users won’t stick.

So the design focused on transparency:

• Explainable matches: Why two people were paired.

• AI as coach: Real-time tips to help chats flow.

• Dynamic profiles:

  • Vibe-check → a live mood board of a user’s chat style.

  • Humor style → icebreakers tailored to their jokes.

Warm, human-facing AI = trust that compounds.

5. Growth & Moat

The business play was as important as the tech:

• Monetization: Pay-per-compute credits instead of rigid subs. Users pay only for the AI juice they need.

• Data moat: More chats → better model → smarter matches → higher retention. A compounding flywheel, tough to copy.

Feedback loops (DPO, self-supervised signals, federated learning) ensured the system stayed sharp, safe, and user-first.

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The Big Take

MIT xPRO gave me more than AI skills. It gave me a mindset: AI alone isn’t enough.

The real unlock is hybrid intelligence systems, where machines handle scale and humans bring sense.

This capstone was my first step in building that bridge.