After shipping several AI-powered features in production, I've learned some lessons the hard way. Here's what I wish I knew when I started.

Lesson 1: Latency Matters More Than You Think

Your users don't care how smart your AI is if they have to wait 10 seconds for a response. We spent weeks optimizing for quality before realizing speed was the actual bottleneck.

What we did: Added streaming responses, optimized prompts for shorter outputs, and implemented aggressive caching.

Lesson 2: Users Don't Read Instructions

We wrote beautiful documentation explaining how to get the best results from our AI features. Nobody read it. The AI needed to work well with whatever input users threw at it.

What we did: Made the system more robust to bad inputs instead of expecting users to change their behavior.

Lesson 3: Costs Scale Faster Than Revenue

AI API calls add up quickly. We had features that cost more to run than they generated in value. Unit economics matter.

What we did: Implemented usage limits, optimized token counts, and killed features that didn't make financial sense.

Lesson 4: Hallucinations Will Happen

No matter how good your prompts are, the AI will occasionally make things up. You need systems to catch this.

What we did: Added verification steps, human review for high-stakes outputs, and clear disclaimers about AI-generated content.

Lesson 5: Start Simple

We over-engineered our first attempt with complex multi-model pipelines. The simple version we shipped later performed just as well.

What we did: Stripped everything back to the simplest possible implementation, then added complexity only where necessary.

The Meta-Lesson

Building with AI is still software engineering. The same principles apply: ship early, iterate based on feedback, and don't let the tech distract you from solving real user problems.

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