1. Token costs will surprise you
Everyone talks about "using the OpenAI API." Nobody talks about what happens when you have 500 users asking 10 questions each, every day.
Token costs scale fast. A well-crafted system prompt that's 800 tokens × every API call = real money. You need to:
At ExplainMate, we reduced per-conversation token usage by 60% through these optimizations while keeping response quality identical.
2. Prompt engineering is an art
There are no "best prompts." There are prompts that work for your specific use case, with your specific model, at your specific temperature setting.
What works for a creative writing tool will make an educational tool give vague, flowery answers. What works for GPT-4 might not work the same way on Claude.
Test everything. Measure output quality systematically. Don't trust prompts you found on Twitter.
3. Latency kills UX more than anything
Users will tolerate a slightly worse answer if it comes in 1 second. They will not tolerate a perfect answer that takes 8 seconds.
Stream responses. Use loading states. Pre-load context where possible. Cache aggressively.
4. The real skill is system design
Using an AI API is easy. Building a system *around* AI that works reliably at scale is hard.
That means: rate limiting, fallback models, error handling, caching layers, authentication, logging, and monitoring.
Most "AI developers" know how to call an API. Very few know how to build the infrastructure around it. That's the actual skill gap.
5. Users will break your prompts
Within 48 hours of launch, someone will ask your AI something you never tested. And it will either fail hard, hallucinate, or do something embarrassing.
Build defensive prompting. Set clear system boundaries. Log everything. Monitor for unusual outputs.
Your AI is only as good as your constraints.