3 Lessons Learned Building AI-Powered Applications
by Hamzah Ejaz, Software Engineer
1. Real-Time Processing Requires Strategic Architecture
Building AI features that feel instant to users while managing API costs and latency is a unique challenge. Through projects like EVA Meet, I learned that the key is strategic caching, streaming responses, and smart pre-processing.
Rather than waiting for complete AI responses, streaming allows users to see results as they're generated. Combined with WebSocket connections and optimistic UI updates, this creates experiences that feel responsive even when processing complex requests.
Top tip
Implement request queuing and rate limiting early. AI API costs can spiral quickly without proper controls, and user experience suffers when requests are throttled unexpectedly.
2. Prompt Engineering is Software Engineering
Writing effective prompts for AI models is as critical as writing good code. Small changes in prompt structure can dramatically affect output quality, consistency, and cost. I've learned to treat prompts as versioned, tested code artifacts.
For production applications, maintaining a library of tested prompts with clear documentation is essential. This includes edge cases, expected outputs, and failure modes. Just like unit tests, prompt testing should be automated and run before deployment.
The best approach combines system prompts for consistent behavior with dynamic context injection for specific use cases. This separation makes prompts maintainable and easier to optimize.
3. Balance AI Features with Traditional Solutions
Not every problem needs AI. Sometimes a well-designed database query, caching strategy, or traditional algorithm is more reliable and cost-effective. The key is knowing when AI adds genuine value versus when it's overengineering.
I've found AI most valuable for natural language processing, content generation, and pattern recognition where traditional approaches struggle. For structured data processing, validation, or deterministic operations, classic programming usually wins.
The sweet spot is hybrid systems: use AI where it excels, traditional code where it's reliable, and create seamless integration between the two.
