Why I Treat AI Models as Components, Not Magic

In the rush to adopt LLMs, many engineering teams treat models as magical black boxes. You throw text in, you get text out. But this leads to fragile, non-deterministic systems.

As an AI Engineer, I argue for treating models like any other stochastic component in a distributed system.

1. Define Interfaces, Not Prompts

Instead of endless prompt engineering, wrap your LLM calls in strict, typed interfaces. Use tools like Pydantic (in Python) or Zod (in JS) to enforce structure on the output.

2. Fail Gracefully

LLMs hallucinate. Your system shouldn't crash when they do. Implement circuit breakers and fallback logic. If the high-intelligence model fails or times out, fall back to a faster, cheaper model or a heuristic rule.

3. Observability is Mandatory

You wouldn't deploy a database without monitoring. Don't deploy an LLM without tracing. Track token usage, latency, and most importantly, semantic drift over time.

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