AI development is a weird mix of regular coding and black magic. You're building systems that need to be reliable and predictable, but also interact with models that sometimes just... decide to do something unexpected. It keeps things interesting.
My approach is to treat AI as a powerful but unpredictable collaborator. Lots of defensive coding, input validation, and fallbacks. The goal is to make AI feel intelligent and helpful while keeping the experience consistent, even when the model is having a bad day.
The Process
Architecture & Model Selection
Figuring out which AI models to use and how to integrate them. Cost, speed, and capability all matter, gotta find the right balance.
Database Design
Building a schema that handles users, AI outputs, and history. Row Level Security is non-negotiable, learned that lesson the hard way.
Prompt Engineering
Writing prompts that actually work consistently. This is way harder than it sounds and takes more iterations than I'd like to admit.
Adaptive Logic
Building algorithms that respond to user behavior in real-time. When it works, it's magic. When it doesn't, it's debugging hell.
Security Paranoia
Rate limiting, injection filtering, auth checks everywhere. People WILL try to break your AI. Plan for it.
Making It Feel Good
Animations, loading states, and micro-interactions that make the AI feel responsive and smart. The polish that separates "works" from "wow".
Tools & Technologies
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