Fine-Tuning vs RAG vs Prompting

A useful way to decide is to name the gap before naming the solution. There are four common gaps: knowledge (the model does not know your facts), format (it knows but answers in the wrong shape), style or tone (it answers correctly but does not sound right), and skill (it cannot reliably perform the task at all, such as a specialised extraction or a domain-specific judgement). Prompting is the first lever for almost every gap because it is cheap, fast to change and easy to test. Clear instructions, a defined output schema and a few examples fix a large share of format and style problems. Its limits are context length, cost per call when prompts grow long, and inconsistency on hard tasks. RAG addresses knowledge. At query time a retriever finds relevant passages from your documents and places them in the prompt, so the model answers from current, citable sources. It suits facts that change, facts that must be traceable, and collections too large to fit in a prompt. Its quality depends heavily on retrieval: if the right passage is not found, the model cannot use it. Fine-tuning trains the model on examples of desired input and output. It is strong at locking in a format, a tone, or a narrow skill, and it can let a smaller model replace a larger one, cutting latency and cost. A common misconception is that fine-tuning is a good way to add facts. It can shift what a model tends to say, but it is an unreliable and hard-to-update way to store knowledge, and it can increase confident errors about material the model saw only a few times. The methods combine. Many production systems use a fine-tuned model that is good at reading retrieved context, driven by a well-designed prompt. The practical order is usually: prompt, then add retrieval if knowledge is missing, then fine-tune when you have evaluation data showing a persistent gap.