Hot take: 95% of "LLM applications" I see on LinkedIn are demos, not products.

The demo: "Look, I connected GPT to my database and it answers questions!"

The production system: It handles 10K concurrent users. It doesn't hallucinate on financial data. It costs $0.002 per query. It has fallback logic when the API is down. It logs everything for compliance. It degrades gracefully under load.

I've shipped both. The demo took an afternoon. The production system took 4 months and a team of five.

The skills that actually matter for LLM engineering aren't prompt writing. They're RAG pipeline optimization, guardrails, output validation, caching strategies, and cost modeling.

If your LLM app works great in a Jupyter notebook but you've never thought about what happens when 1,000 people use it simultaneously — you're building a toy, not a product.

And that's fine for learning! But don't confuse the two.