# AI Tools for the Modern Builder: From Dot to Study Buddy

AI isn't just a buzzword anymore - it's a superpower for builders. Over the past year, I've built several AI-powered products, and I want to share what I've learned about building with AI in 2025.

## My AI Projects

### Dot: AI Therapist for iOS

A voice-assisted app built with Swift that acts as a personal AI therapist. Features include:

Mood tracking
Voice-based journaling
AI chat interface powered by Gemini
Privacy-first design (all data stays on device)

### Study Buddy: AI Learning Companion

An AI study buddy that generates personalized notes and quizzes for any topic. Built to help students learn faster and retain better.

## What I've Learned

### 1. AI is a Tool, Not a Solution

The biggest mistake I see is people treating AI as magic. It's not. AI is a tool that amplifies human capability.

Bad approach: "Let's add AI to our app!" Good approach: "Our users struggle with X. Can AI help solve that?"

For Dot, the problem was clear: people need a judgment-free space to talk about their feelings. AI provided a solution - an always-available, empathetic listener.

### 2. Prompt Engineering is Real Engineering

The quality of your AI product depends heavily on your prompts. I spent weeks iterating on prompts for Dot to make the AI:

More empathetic
Less generic
Context-aware
Appropriately boundaries

Example from Dot:

<code/>
Bad prompt: "You are a therapist. Help the user."

Good prompt: "You are a compassionate AI companion named Dot.
Your role is to listen actively, ask thoughtful questions,
and help users reflect on their feelings. Never diagnose
medical conditions. If the user mentions self-harm or
severe mental health issues, encourage them to seek
professional help immediately."

### 3. The API Choice Matters

I've worked with multiple AI APIs:

OpenAI (GPT-4): Expensive but excellent for complex reasoning
Google Gemini: Good balance of cost and quality, great for voice
Claude (Anthropic): Excellent for long-form content and analysis
Local Models: Fast, private, but require more setup

For Dot, I chose Gemini because:

Better voice interaction capabilities
More affordable for a student project
Fast response times
Good context handling

### 4. Privacy is Non-Negotiable

Especially for an AI therapist app, privacy was critical. My approach:

All conversations stored locally on device
Minimal data sent to AI API (only the current conversation)
No user tracking or analytics
Clear privacy policy

Users need to trust that their data is safe.

### 5. AI Hallucinations are Real

AI models sometimes make things up. For Study Buddy, this was a problem - we couldn't have it teaching false information.

Solutions:

Fact-checking against reliable sources
Clear disclaimers about AI-generated content
User feedback mechanisms to report issues
Temperature tuning (lower temperature = more factual, less creative)

## Building AI Products in Practice

### Start with the User Problem

Don't build AI features because they're cool. Build them because they solve real problems.

For Study Buddy:

Problem: Students struggle to create effective study materials
AI Solution: Generate customized notes based on learning style
Value: Saves hours of study prep time

### Iterate on the Experience

The first version of Dot was terrible. The AI responses felt robotic. The voice interface was clunky. The UI was confusing.

I iterated based on user feedback:

Version 1: Basic chat interface
Version 2: Added voice input
Version 3: Mood tracking and patterns
Version 4: Better AI prompts for more natural conversations
Version 5: Journal entries linked to mood

Each iteration made the product more human-centered.

### Optimize for Speed

Nobody wants to wait 10 seconds for an AI response. Speed matters.

Techniques I use:

Streaming responses (show text as it's generated)
Local caching for common queries
Background processing where possible
Loading states that feel fast (skeleton screens, not spinners)

### Handle Edge Cases

AI is unpredictable. Plan for failures:

Network timeouts
API rate limits
Inappropriate content generation
Context length limits

For Dot, I implemented:

Graceful fallbacks for API failures
Content moderation checks
Clear error messages
Offline mode with cached responses

## The Economics of AI Products

Let's talk money. AI APIs aren't cheap.

Cost breakdown for Dot (per 1000 users/month):

Gemini API calls: ~$50-100
Backend hosting: $20
iOS Developer Account: $99/year
Total: ~$170/month + development time

Making it sustainable:

Start with a free tier (limited conversations)
Premium subscription for unlimited access
Optimize API usage (cache common responses)
Use smaller models for simple tasks

## Tools I Use for AI Development

LangChain: For complex AI workflows
OpenAI Playground: For prompt testing
Postman: For API testing
Xcode: For iOS development
Firebase: For analytics and crash reporting

## Common Pitfalls

Over-relying on AI: Some tasks don't need AI. Sometimes a good algorithm beats AI.
Ignoring latency: Fast, simple solutions beat slow AI solutions
Poor prompt design: Garbage in, garbage out
No user control: Let users edit/regenerate AI outputs
Assuming AI is always right: Always include human oversight

## The Future I See

AI is going to be in everything, but the winners will be products where AI is invisible - it just makes things work better.

Think:

Apps that understand your context without you explaining
Study tools that adapt to your learning speed
Therapist apps that remember your history and patterns
Code editors that understand your intent (like what you're reading this on!)

## Advice for Builders

Want to build with AI?

Start Small: Build a simple AI feature first
Focus on UX: The AI should enhance the experience, not complicate it
Iterate Fast: Ship early, get feedback, improve
Manage Costs: Start with free tiers, optimize before scaling
Privacy First: Especially with sensitive data

## What I'm Building Next

I'm exploring:

AI-powered code review tools
Smart scheduling assistants
Personalized learning paths based on user progress

The possibilities are endless when you combine AI with a real understanding of user needs.

Building something with AI? I'd love to hear about it. DM me on Instagram or connect on LinkedIn.