Fine-tuning has gone from "research lab toy" to a first-class production technique for AI engineers. With LoRA-class adapters, modern alignment algorithms (DPO, GRPO, RLVR), and serving stacks like vLLM, you can ship a custom model on a single H100 — sometimes on a single 4090. But the question isn't can you fine-tune. It's: should you? This guide is the engineering checklist I wish I'd had two years ago. It covers the decision tree, the modern toolchain, the gotchas, and the EU compliance constraints you can't ignore in 2026. 🇪🇺 Romanian / EU readers: the full hands-on Romanian-language program is at Fine-Tuning și Adaptarea Modelelor AI — Enterprise Edition . It includes a complete end-to-end project, EU AI Act governance, and FinOps modeling. TL;DR Don't fine-tune first. Try prompting → RAG → fine-tuning. In that order. LoRA / QLoRA is the default in 2026. Full fine-tuning is rarely the right call. Alignment ≠ SFT. SFT teaches format ; DPO/GRPO/RLVR teach preferences and reasoning .…