Most engineers approach LLM fine-tuning backwards.

They grab a dataset, throw it at LoRA with default hyperparameters, and wonder why their model hallucinates worse than the base. Sound familiar?

The problem isn't the technique — it's the sequence.

The 3 mistakes I see constantly:

1. Skipping data engineering. Your fine-tuned model is only as good as your dataset. Garbage formatting, inconsistent instruction-response pairs, and tokenization mismatches silently destroy model quality. Most tutorials skip this entirely.

2. Defaulting to LoRA without understanding the tradeoffs. LoRA is powerful, but choosing the wrong rank, targeting the wrong modules, or misunderstanding quantization artifacts (QLoRA) leads to models that look fine on benchmarks but fail in production.

3. Ignoring alignment. A supervised fine-tuned model isn't production-ready. Without RLHF or DPO, you're shipping a model that hasn't learned to distinguish good outputs from bad ones.

What actually works:

A structured progression: Foundations → Data Engineering → Parameter-Efficient Methods → Full Fine-Tuning → Alignment → Deployment.

Each stage builds on the last. Skip one, and you'll hit a wall later.

I built a complete course covering exactly this — 6 modules, 15 lessons, from zero to deploying production fine-tuned models. Hands-on with Mistral-7B, DeepSpeed, FSDP, DPO, and real deployment pipelines.

If you're serious about shipping fine-tuned LLMs that actually work, this is the shortest path I know.