Vector similarity is not semantic similarity, and that gap was hiding in plain sight in the audio validation pipelines I built at scale.

Two sentences would come back near-opposite in meaning. In embedding space they sat almost on top of each other. The system had no way to tell them apart. One was right. One was wrong. Both looked equally confident.

I started calling this the Negation Blindness Trap. Transformers aren't built to catch logical opposites, only statistical patterns, so when negation flips a sentence's meaning, the embedding barely moves. Most teams don't notice until it's already shipped.

This talk walks through where I ran into that problem building validation pipelines at Humyn Labs, why it shows up more in RAG systems than people expect, and what I built to catch it: a validation layer with semantic drift detection, confidence thresholds, and fallback logic for when the system genuinely doesn't know.

If you're building with embeddings or RAG, this isn't theory. It's what broke, how I found it, and what I changed.