Two co-op grocery loyalty defect notes—“points vanished” and “balance dropped”—land near each other in a numeric space so the model treats them as related. Which LLM basic does this describe?
Select an answer to reveal the explanation.
Short Explanation
Neighbors on a map share a neighborhood even if their street names differ. Embeddings turn tokens into numeric vectors so similar meanings cluster. That is not the chopping step and not the size of the prompt window.
Full Explanation
Embeddings represent tokens as vectors that capture semantic meaning and context, which is why paraphrased defect notes can land close together. Tokenization is the prior split into processable units; the context window is the token budget of prior text the model can still consider. Confusing embeddings with those other basics leads testers to misread why related wording is grouped. Non-determinism concerns varied outputs from probabilistic generation, not the definition of an embedding space.