Fine-Tuning vs Prompting, Plainly

When training your own model is - and isn't - worth it: prompting steers at request time, RAG adds knowledge, fine-tuning changes the model's default behavior, and the no-nonsense order is to try them cheapest-first.

1. Three Ways to Steer a Model The three ways to change what a model gives you: prompting changes the instructions at request time, RAG injects knowledge at request time, and fine-tuning adjusts the model's weights to change its default behavior - RAG adds knowledge, fine-tuning teaches behavior.
2. What Fine-Tuning Actually Involves The real work of fine-tuning: building a curated dataset of high-quality example pairs (where most of the cost lives), running the training, hosting and serving the tuned model, the lighter LoRA approach, and how you'd actually know if it worked.
3. Choosing - the No-Nonsense Order The decision order: try prompting, then add RAG, then fine-tune - because each step costs more and locks you in more. Fine-tune for consistent format/style/tone or a narrow task at scale, never to teach facts, and never before prompting is exhausted, with a decision table and the traps to avoid.