I've had AI features in production for about six months now. Long enough to learn what works and what doesn't.

This isn't about integration. It's about what happens after.

Latency Is a Problem

AI APIs are slow compared to normal database queries. 2-3 seconds is common. Sometimes longer.

Users notice. Some assumed the app was broken.

Solutions that helped:

• Show a loading state immediately
• Use streaming for long responses
• Process in background and notify when done
• Cache aggressively

The UX work around AI is as important as the AI itself.

Consistency Is Hard

Same input, different outputs. That's how these models work. But users expect consistency.

If they ask the same question twice and get different answers, they lose trust.

I added temperature settings and seed values where supported. Lower temperature means more consistent outputs. Not perfect, but better.

Costs Scale Weirdly

Normal server costs scale with users. AI costs scale with usage per user.

One power user can cost more than a hundred casual users. I had to add per-user limits and think about pricing differently.

Some apps charge extra for AI features. Makes sense now that I understand the economics.

Model Updates Break Things

OpenAI updates their models. The same model name can behave differently month to month.

I had prompts that worked fine, then suddenly gave worse results after an update. No warning.

Testing is essential. I have a set of test prompts I run after any model update. Catches regressions before users notice.

Context Windows Matter

You can only send so much text to an AI. Hit the limit and it just stops reading.

For my summarization feature, long documents would get truncated. The summary would be incomplete.

Had to implement chunking. Split long content, summarize each chunk, then summarize the summaries. More complex but necessary.

Error Handling Gets Complex

AI can fail in many ways:

• API timeout
• Rate limit hit
• Invalid response format
• Content filtered
• Model overloaded

Each needs different handling. Rate limits should back off. Timeouts should retry once. Filtered content should tell the user why.

I have a whole error taxonomy now. Never thought AI errors would need this much structure.

Users Try Weird Things

Prompt injection is real. Users try to make your AI say inappropriate things or reveal system prompts.

I learned to:

• Keep system prompts minimal
• Validate and sanitize user input
• Filter outputs before displaying
• Monitor for abuse patterns

Most users are fine. But a few will test every boundary.

Logging Is Essential

I log every AI interaction. Input, output, latency, cost, errors.

This helps with:

• Debugging weird responses
• Understanding usage patterns
• Optimizing prompts
• Tracking costs
• Identifying abuse

Storage costs for logs are nothing compared to API costs. Worth it.

The Value Question

After six months, I keep asking: is this worth it?

For some features, absolutely. Users love the auto-categorization. It saves them real time.

For others, not sure. The AI-generated suggestions are fancy but rarely used.

I'm keeping what works and cutting what doesn't. AI isn't magic. It's another tool that needs to prove its value.

What I'd Tell Past Me

• Start with one focused AI feature, not ten
• Budget 3x what you think you'll spend
• Cache everything possible
• Plan for failures from day one
• Watch your logs religiously

AI in production is different from AI in demos. The demos are easy. The production part is where you learn.