Everyone's adding AI to everything now. I wanted to see what the hype was about. So I added GPT to my app.

The integration was surprisingly easy. The aftermath was more complicated.

The First Integration

OpenAI's API is straightforward. Get an API key, install the package, make a call:

const response = await openai.chat.completions.create({
  model: 'gpt-4o-mini',
  messages: [{ role: 'user', content: prompt }]
})

That's basically it. I had something working in an hour.

The hard part wasn't the code. It was figuring out what to actually use AI for.

Finding the Right Use Case

I tried to make AI do too much at first. Generate entire features, write complex content, answer any question.

Turns out AI is better at smaller, focused tasks:

• Summarizing long text
• Suggesting tags or categories
• Improving user-written content
• Answering specific questions about app data

My killer feature ended up being auto-generating descriptions for user uploads. Nobody likes writing descriptions. AI does it acceptably well.

Prompt Engineering Is Real

Same question, different phrasing, wildly different results.

I spent hours tweaking prompts. Adding context, specifying format, giving examples. The difference between a good and bad prompt is huge.

Things that helped:

• Be specific about the output format you want
• Give examples of good responses
• Include relevant context, but not too much
• Tell it what NOT to do

The Cost Problem

API calls add up fast. Each request costs money. Users don't care about your API bill.

I made every mistake:

• Not caching responses for similar inputs
• Calling the API on every keystroke
• Using GPT-4 when 3.5 was fine
• No rate limiting per user

My first bill was a shock. Had to add limits fast.

Rate Limiting and Caching

Now I:

• Cache responses for identical inputs
• Rate limit per user (10 requests per minute)
• Use cheaper models for simple tasks
• Queue expensive operations instead of real-time

The user experience is slightly worse. But I can afford to keep the feature.

Handling Failures

AI APIs go down. They time out. They return weird stuff.

Always have a fallback. If AI fails, show a helpful message or skip the feature gracefully. Don't let your whole app break because OpenAI is having issues.

try {
  const suggestion = await getAISuggestion(content)
  return suggestion
} catch (error) {
  console.error('AI failed:', error)
  return 'Unable to generate suggestion'
}

User Expectations

Users expect AI to be magic. It's not.

I had to add disclaimers. "AI-generated suggestions may be inaccurate." Some users were treating AI outputs as facts.

Also important: let users edit or reject AI suggestions. Never auto-publish AI content without review.

Streaming Responses

For longer outputs, streaming is way better than waiting. The user sees text appearing instead of staring at a spinner.

OpenAI supports streaming. Takes more code but the UX improvement is worth it.

Was It Worth Adding?

Yes, but with caveats.

Users like the AI features. They feel modern. But the maintenance and cost overhead is real.

Would I add AI to every project? No. Does it make sense here? After some iteration, yeah.

The key is finding the right use case. AI that saves users time is valuable. AI for the sake of AI is just expensive.