A LoRA that reproduces its training images perfectly is not a well-trained model. It is a memorization device. This article argues that generalization within the trained concept is the right quality criterion for small-dataset LoRA fine-tuning, and proposes a five-tell diagnostic framework — base capability degradation, concept narrowing, caption rigidity, entanglement leak, and visual signature reproduction — for identifying when a LoRA has crossed from learning into memorization. We then describe a chained training schedule we use in our own work, which rotates through dataset subsets before reintroducing the full combined dataset for a final consolidation phase. This methodology has roots in early Stable Diffusion 1.5-era practitioner trainers, where dataset switching was first-class in the UI; in modern trainers it must be reconstructed manually. We report a paired A/B run on Qwen-Image at two scales — a 244-image illustration style LoRA and a 27-image character LoRA — comparing chained training against a monotonic straight-baseline twin under matched conditions. Both runs produce competent results that pass the five-tell diagnostic without obvious failure on either side, and the hyperparameter recipe we use proves itself viable for Qwen-Image at these scales. We observe specific signals of chained-side flexibility advantages, but the gap is not dramatic enough at these dataset sizes to claim a general methodological advantage from this experiment alone. The natural next test is at smaller and harder dataset sizes where overfitting is more likely to surface; we identify that as the experimental program this position paper opens onto rather than closes.