Text embeddings from large language models are powerful, but often opaque. When an embedding-based classifier misbehaves, it is hard to tell whether the embedding itself is flawed or the classifier is using it poorly. This article introduces a concrete workflow for inspecting Scikit-LLM embedding spaces using three complementary tools: probing classifiers, UMAP visualization, and SHAP values.
Probing classifiers are small models trained on top of embeddings to test whether specific properties—such as sentiment or topic—are recoverable from the vector representation. If a probe performs well, the embedding likely encodes that information; if it fails, the signal may be missing or unrecoverable. This gives a first pass on embedding quality.
UMAP then provides a visual check by reducing the high-dimensional embedding space to two dimensions, making it easier to spot clusters, outliers, or overlapping categories. Finally, SHAP values explain individual predictions by attributing each feature's contribution, helping practitioners understand why a particular text was classified a certain way. Together, these methods turn a black-box embedding pipeline into an analyzable system for debugging and trust-building.