Fine-tuning sounds powerful, and there is a temptation to start with it. But it is costly, complex, needs data and updates hard. Almost always it is worth first trying simpler approaches — often they solve the task without tuning. The order: prompting to prompt examples (few-shot) to RAG (for knowledge) to and only if not enough, fine-tuning. Understanding this order saves a lot of time and money.
Fine-tuning is a last resort; first try prompting, examples and RAG.
The ladder of approaches (from simple to complex)
A good prompt: a clear instruction, a system prompt with a role and rules. Often this is enough.
Prompt examples (few-shot): give the model a few "input to output" examples in the prompt. Powerfully tunes behavior without training.
RAG: if you need knowledge over your data — RAG, not tuning.
Fine-tuning: only if prompting and examples do not give the needed behavior stably, and it is justified.
Why start with prompting rather than fine-tuning right away?
Practical reasons to start simple: 1) Prompting often solves the task. Modern models are very flexible — a good system prompt + a few examples (few-shot) in the prompt tune behavior, style, format often as well as fine-tuning but WITHOUT its costs. Try this BEFORE fine-tuning — often it turns out fine-tuning is not needed. 2) Fine-tuning is costly and complex: you need to prepare a quality data set (hundreds-thousands of examples), run training, evaluate, deploy. That is time, money, expertise. A prompt can be changed in a second, tuning requires retraining. 3) Flexibility: a prompt is easy to change and iterate. A fine-tuned model is hard and costly to change (retrain). If the task may change — prompting is more flexible. 4) Updating: if behavior/requirements change, a prompt is fixed instantly, but a fine-tuned model must be retrained. 5) Few-shot is especially powerful: examples right in the prompt ("here is how: [example1] [example2]") give the model a behavior sample without training. This is an underrated tool — often replaces fine-tuning. When fine-tuning is still justified (after the simple was not enough): — Prompting and few-shot do not give the needed behavior stably. — You need a very specific behavior/format hard/costly to explain with a long prompt each time. — Large volume: if you make millions of calls, tuning can shorten prompts (of the needed behavior) and reduce cost at scale. — You have a quality training data set. Practical rule: go up the ladder from the bottom. Start with a prompt. Not enough — add examples (few-shot). Need knowledge — RAG. And only if all this does not give the needed BEHAVIOR stably and you have data — consider fine-tuning. A common mistake is to jump straight to tuning ("it is powerful"), spend weeks and money, though the task was solved by a good prompt. Fine-tuning is a specialized tool for cases where the simpler is not enough, not a first choice.
Few-shot — an underrated alternative
Let us highlight few-shot: giving the model a few "input to output" examples right in the prompt. This powerfully tunes behavior without any training — the model follows the sample from the examples. Often few-shot replaces what people want to fine-tune for. Before thinking about fine-tuning, try giving good examples in the prompt: often it is enough, and it is far cheaper and more flexible.
When fine-tuning is justified
Fine-tuning becomes sensible when: prompting and few-shot do not give the needed behavior stably; you need a very specific behavior/format costly to explain with a prompt each time; a large call volume where tuning will shorten prompts and cost at scale; and you have quality training data. It is a specialized tool for cases where the simpler was not enough — not a first choice. Go up the ladder from the bottom.
Rule: go from simple — prompt to few-shot examples to RAG (for knowledge) to fine-tuning (only if the simpler was not enough and justified). Few-shot often replaces tuning; do not jump to it right away.