embedding — A list of numbers representing a piece of text's meaning, so that similar meanings end up numerically close and can be searched.
grounding — Tying a model's answers to checkable sources — retrieved documents, live data, tool results — instead of letting it answer from memory alone.
Why it matters
A clear walkthrough of how free-text words become numeric vectors — from one-hot encoding to learned embeddingA list of numbers representing a piece of text's meaning, so that similar meanings end up numerically close and can be searched.Full definition → — groundingTying a model's answers to checkable sources — retrieved documents, live data, tool results — instead of letting it answer from memory alone.Full definition → the intuition behind the embedding models and vector search that agentic engineers rely on daily.
Key quotes
“One of the simplest transformation approaches is to do a one-hot encoding in which each distinct word stands for one dimension of the resulting vector and a binary value indicates whether the word presents (1) or not (0).”
