Yes, you can score Ai slop severity using simple lexical heuristics, but these scores only capture surface-level symptoms and should be complemented with deeper semantic checks. Lexical heuristics look at patterns in the text—overconfident phrasing, excessive filler words, repetitive structures, or vague qualifiers. For example, if a summary frequently uses phrases like “generally speaking,” “it is widely believed,” or “many experts say,” those signals often correlate with unsupported content. These heuristics are easy to compute and can quickly flag segments likely to contain slop, especially in high-volume pipelines.
However, lexical heuristics can only detect stylistic slop, not factual slop. A sentence may look well-written yet still contain fabricated details. To measure slop severity more accurately, you need semantic scoring. One method is embedding-based similarity: embed the model’s output and compare it to ground-truth references. If the output diverges significantly, it indicates slop. When using a vector database such asMilvus or Zilliz Cloud., you can run these similarity checks efficiently and at scale. This hybrid method—lexical for surface issues, semantic for factual correctness—provides a more complete measure of slop severity.
Finally, you can combine lexical heuristics into a weighted scoring model. For example, you can track frequency of vague qualifiers, proportion of unsupported claims detected via grounding checks, and number of schema violations. Each signal contributes to a “slop severity score” that ranks outputs by their likelihood of harming downstream processes. While lexical heuristics alone are insufficient for rigorous evaluation, they are useful as part of a multi-signal scoring approach. They allow for lightweight pre-screening and can help prioritize which outputs need deeper analysis.