The Irresistible Temptation of a New LLM

When a new LLM drops and your production model cries

There’s a stable, well-tested, production-ready model.
It went through weeks of fine-tuning.
It has guardrails. It logs. It scales.

But then…
🚨 New LLM just dropped on HuggingFace with +0.3 on MMLU 🚨

And suddenly, you’re that guy.
The one you swore you’d never become.
Again.

The real job

Being an AI Engineer isn’t just “prompt + deploy”.
It’s tradeoffs. It’s reliability.
It’s monitoring a hallucination spike at 2AM because someone tried to replace your model mid-sprint.

We juggle benchmarks, latency, budget constraints, and downstream product teams: all while trying to convince leadership that “Open Source”’ doesn’t mean “Free And Easy”.

💬 Final thoughts

The AI Engineer role is still misunderstood: often romanticized, underestimated, or oversimplified. It’s not just about prompts, or training GPT clones. It’s engineering: systems, pipelines, latency, logging, versioning, and everything in between.

If you’re getting into this field, or working in it and feeling frustrated: you’re not alone.

This blog exists to share that reality. Every week. With code, insights, failures and lessons.

Let’s enhance the future of AI together.