Decide
First question: should you even fine-tune? Often RAG or prompting is cheaper and better. Use the decision tool below.
Live · LLM · ML Engineering
LLM fine-tuning walkthrough
What does it actually take to fine-tune a large language model on your company’s data? A 2-core CPU can’t run GPU training, so we don’t fake it. Instead, the parts that are deterministic are real and computed live: formatting training data, LoRA parameter math, GPU cost estimates, and the fine-tune vs RAG vs prompt decision. The GPU training step is explained honestly, not simulated.
Should you fine-tune at all?
Recommendation
Answer the questions and we’ll suggest fine-tune, RAG, prompt, or a hybrid.
How it works
Collect & format
Gather instruction/response pairs and convert them to the exact chat-template JSONL the trainer needs. Try the formatter.
Prep
Clean, deduplicate, split into train/validation, and check for leakage and label noise.
Choose method
Full fine-tune vs LoRA vs QLoRA. LoRA trains a tiny fraction of parameters — see the calculator.
Train & evaluate
Run on GPUs, watch the loss, then evaluate on a held-out set with win-rate and regression checks — not just loss.
Deploy & monitor
Merge or serve the adapter, monitor quality and drift, and retrain as your data evolves.
Want us to fine-tune a model on your data?
We run end-to-end fine-tuning: data collection and labeling, formatting, LoRA/QLoRA training on GPUs, evaluation against your golden set, regression checks, and deployment with monitoring — or we help you decide it should be RAG instead.
Ready to start
Turn one AI use case into measurable production value.
Book a 30-minute consultation. We will walk through the use case, sketch the value case, and tell you honestly whether we can help.