A CLIP-style score estimates how well an image and a text description align in a shared representation space. It is useful because it is fast and repeatable. It is dangerous when treated as a definition of quality.
What a score can answer
Use it to compare controlled variants: did the image move closer to the requested subject, attribute or composition? Keep prompt, seed policy and evaluation set documented. A score is strongest as one signal in a repeated experiment, not as a trophy for a single image.
What it cannot answer
Similarity is not factual correctness, originality, safety, aesthetics or audience fit. A model can reward a familiar-looking image even when details are wrong. Training data and labels can also encode uneven representation. Test difficult examples deliberately: uncommon objects, cultural context, negation and fine spatial relations.
A practical review loop
Record the score, then ask three human questions: Is the requested action correct? Is the composition usable? Does the output introduce a harmful or misleading assumption? The detailed CLIP Score guide covers the formula; this page supplies the decision discipline around it.