When should you NOT fine-tune?

If you haven't exhausted prompt engineering + few-shot + RAG. Those are often enough and much cheaper. Fine-tune when stability, speed, or tone demands it.

How much training data?

Simple tasks: 100–500 examples may suffice. Complex domains: 1,000–10,000+. Quality > quantity — 500 great examples beat 5,000 noisy ones.

Hungarian fine-tuning?

Yes. Llama 3.1, Qwen 2.5, Mistral handle Hungarian and improve with fine-tuning. Hungarian RAG + fine-tuned generation is the strongest combo.

LLM fine-tuning

LLM fine-tuning — when prompt engineering isn't enough

If the base model doesn't understand your domain, brand voice, or specialised terminology, fine-tuning is the answer. UseAIEasily ships LLM fine-tuning from Budapest for Hungarian and international clients.

When to fine-tune

Domain terminology the base model doesn't handle well
Brand voice and tone that must stay consistent
Structured output (JSON, XML) that must be exact every time
Specialised domain (legal, medical, financial) where base models stay generic
Cost: a fine-tuned smaller model is cheaper than GPT-4 + prompt engineering

What we deliver

Dataset prep

Collection, cleaning, labelling, augmentation, train/val/test split. Multilingual corpus handling.

Model selection

OpenAI (GPT-4o-mini, GPT-4.1), Anthropic (Claude context engineering), open-source (Llama 3.1, Mistral, Qwen) on Together or self-hosted vLLM.

Training and eval

LoRA or full fine-tune, hyperparameter tuning, eval suite based on real business metrics — not just loss.

Deployment

Managed or self-hosted vLLM in EU region. A/B test against base, monitoring.

Timeline and cost

PoC fine-tune: 3–4 weeks, €10k–€20k. Production fine-tune: 6–10 weeks, €25k–€60k (GPU compute separate).

Let's fine-tune your model

30-minute call to scope data, goals, and budget. If prompt engineering is enough, we'll tell you that too.

Book a discovery call