Anomaly-Detection on Thoughts and code

Z-Scores Over Gut Feel: Teaching Similarity Scores What “Weird” Actually Means

Thu, 02 Apr 2026 08:00:00 +0000

A fixed similarity threshold is easy to implement and surprisingly hard to justify. It assumes your data behaves consistently, your model behaves consistently, and your definition of “normal” does not drift. None of those assumptions tend to hold for long.

Up to this point in the series, we have treated cosine similarity as a raw signal. A number comes out, we compare it to a cutoff, and we move on. Here we start treating those numbers as a distribution instead.

Context Beats Magnitude: Why Embedding Sentences Outperforms Raw Numbers in Anomaly Detection

Wed, 01 Apr 2026 08:00:00 +0000

54.20. That’s what the transaction amount field contains. Embed that string and the model learns something about the number 54.20: that it sits near 54.19 and 54.21, that it’s a plausible price for groceries or a restaurant meal. It doesn’t learn anything about where the money went or when. This post uses a deliberately reductive fraud-shaped example to make a narrower point: formatting structured fields as a sentence gives the embedder more context than a raw scalar ever can.