How I structure AI projects for long-term maintainability
Lessons from building AI systems that outlast the initial excitement — and the patterns I keep coming back to.
The hardest part of building AI-powered systems isn’t making them work. It’s making them stay working six months later, when the team has changed, the underlying model has been updated, and the original author is three projects deep into something else.
Here’s what I’ve learned.
Treat LLM calls as external I/O, not magic
The biggest architectural mistake I see is treating LLM calls as some special, intelligent layer in the system — when in practice they behave exactly like a slow, expensive, occasionally unreliable API.
That means the same patterns apply: circuit breakers, timeouts, caching, retry logic with backoff, and proper error boundaries. Nothing special. When you design the LLM layer this way, the rest of the system becomes dramatically easier to test and maintain.
Separate prompt logic from application logic
Your prompts will change. Constantly. Model updates, evaluation feedback, edge case discovery — prompts are living artifacts that evolve on a different cadence than your application code.
Keep them out of your business logic. I treat prompt templates as configuration, versioned alongside the models they target. This makes A/B testing prompts trivial and rollbacks clean.
Instrument everything from day one
AI systems fail silently in ways that traditional software doesn’t. A model might return a subtly wrong answer that looks correct until someone catches it weeks later.
The only defense is observability: log every input and output, track latency distributions, sample for quality review. It sounds expensive; it’s not. The cost of not knowing is higher.
Start with the simplest thing that works
Every project starts with someone proposing an agent with memory, RAG, tool use, and a custom fine-tune. I’ve learned to push back hard on this. Start with a single prompt. Measure it. Add complexity only when the measurement tells you to.
The best AI architectures I’ve seen are the ones that solved the problem with the least machinery possible.