Most teams building with LLMs are building demos. The architecture that gets you from "look, it works in a notebook" to "this handles 10K concurrent users with predictable latency" is fundamentally different.
This isn't about prompt engineering. It's about system design.
#The Prototype Trap
Every AI product starts the same way: someone wraps an API call in a Flask endpoint and shows it to the team. Everyone gets excited. Then someone asks: "What happens when the model is slow? What happens when it hallucinates? What happens when we need to swap providers?"
The gap between a working prototype and a production system isn't code quality — it's architectural decisions made (or deferred) in the first two weeks.
The decisions that matter aren't the obvious ones. They're the structural choices that compound:
- Orchestration layer: Are you calling the model directly, or through an abstraction that lets you swap, retry, and cache?
- Evaluation pipeline: How do you know the model's output is good enough? Not "it looks right" — measurably good enough.
- Fallback architecture: What happens when the model fails? Not crashes — produces subtly wrong output.
#The Orchestration Pattern
The first architectural decision is the most consequential: how do you talk to the model?
// Naive: direct API call
const response = await openai.chat.completions.create({
model: 'gpt-4',
messages: [{ role: 'user', content: prompt }],
});
// Production: orchestrated with fallback, caching, and metrics
const response = await orchestrator.execute({
intent: 'classify-ticket',
input: { ticket },
config: {
primaryModel: 'gpt-4',
fallbackModel: 'claude-3-haiku',
cacheTTL: 3600,
maxLatencyMs: 2000,
retries: 2,
},
});
The orchestrator isn't just a wrapper. It's the control plane for your AI system. It handles:
- Model routing — which model for which task, based on cost/latency/quality tradeoffs
- Caching — semantic cache that doesn't just match exact inputs
- Observability — every call logged with latency, token count, and quality score
- Circuit breaking — degrade gracefully when a provider is down
Ship the architecture that lets you delete code, not the one that lets you add it.
#Evaluation Is Infrastructure
Most teams treat evaluation as a manual step. Someone reads the output, says "looks good," and ships it. This doesn't scale.
Evaluation isn't a feature — it's infrastructure. Build it into the pipeline the same way you'd build logging or monitoring. Every model call should have a measurable quality signal attached to it.
The evaluation pipeline has three layers:
- Automated checks: Regex, schema validation, length constraints. Catches the obvious failures.
- Model-graded evaluation: Use a cheaper/faster model to grade the output of your primary model. Not perfect, but scales.
- Human-in-the-loop: Sample-based review for edge cases and calibration. Expensive, but necessary for trust.
#The Cost Conversation
Here's the part nobody writes about: AI products are expensive to run. Token costs compound. Latency budgets are real. And the pricing models change quarterly.
The architectural response to cost isn't "use a cheaper model." It's:
- Cache aggressively — most requests cluster around common patterns
- Route intelligently — not every request needs the best model
- Batch where possible — amortize overhead across multiple inputs
- Measure obsessively — you can't optimize what you can't see
#What I Shipped
The system I built handles classification, extraction, and generation across three product surfaces. It processes ~50K requests daily with p95 latency under 800ms and a model-graded quality score above 0.92.
The architecture fit on a whiteboard. That was the point.
The best systems aren't complex. They're clear.