What Is Fine-Tuning?
Fine-tuning further-trains an LLM on your examples to change its style or teach a narrow skill. When it beats RAG, and when it's the wrong tool.
Fine-tuning: further-training a pre-trained model on your own examples to change how it behaves (its tone, format or a narrow skill) rather than giving it new facts to look up.
Fine-tuning is further-training a pre-trained model on your own examples to change how it behaves (its tone, format or a narrow skill) rather than giving it new facts to look up.
You assemble input-output examples and continue training so the model internalises the pattern. It’s ideal for consistent formatting, a house voice, or a classification task, and can let a smaller (cheaper) model match a larger one on a specific job.
Why it matters
The common mistake is reaching for fine-tuning to add knowledge. That’s usually RAG’s job, and it’s cheaper and stays current. Fine-tuning works best for behaviour, not facts, and it needs a quality labelled dataset most teams underestimate.
How Finzarc thinks about it
We fine-tune when the goal is behaviour or cost (a small fine-tuned model beating a big general one), and use RAG when the goal is knowledge, often combining them. Scope your case with us or read the RAG vs fine-tuning comparison.
Related
Want this built into your business rather than just explained? See what we ship or book a 30-minute scope call.
Questions, answered.
What does fine-tuning an LLM mean?
It means continuing to train a pre-trained model on your own input-output examples so it adopts a behaviour (a tone, a format, or a narrow skill). It changes how the model acts, not what facts it knows.
When should I fine-tune instead of using RAG?
Fine-tune for behaviour (consistent style/format, a specialised classification, or making a small model match a big one). Use RAG for knowledge that changes: it's cheaper to keep current and can cite sources.
How much data do I need to fine-tune?
Less than people expect for style tasks (hundreds of good examples can help) but quality matters more than volume. The real cost is curating a clean, representative labelled dataset.
30 minutes with the founding team. Bring the problem; leave with a scope, a timeline, and the number it should move.