Fine-tuning changes how a model behaves. It does not reliably add knowledge, which is the misconception that wastes the most money in this area. If the problem is that the model does not know your product catalogue, retrieval solves it, stays current and costs a fraction. If the problem is that the model will not hold a house style, follow an unusual output format or match a visual look, fine-tuning is the right instrument.
Image models are where it earns its keep most visibly: a LoRA trained on a few dozen consistent images will hold a look across a whole set in a way no prompt reliably does. On the text side, the honest sequence is to exhaust prompting and retrieval first, because a fine-tune is a fixed asset that has to be maintained, re-run when the base model moves on, and evaluated against the thing it replaced.
Related terms
LoRA (low-rank adaptation)
A way of fine-tuning that trains a small add-on layer instead of the whole model, producing a file of a few megabytes rather than a few gigabytes.
Large language model (LLM)
A model trained on enormous amounts of text to predict what comes next, which turns out to be enough to write, summarise, translate and reason through many tasks.
RAG (retrieval-augmented generation)
Looking up relevant documents first and putting them in the prompt, so the model answers from your material instead of from memory.
The bench this belongs to
Generative AIStable Diffusion and ComfyUI wired into the place where your content actually gets made, with a model fine-tuned so everything comes out looking like you.
