Vector Embeddings and Semantic Similarity
The mathematical core of retrieval: questions and page fragments are converted into high-dimensional coordinates, matched by the closeness of their meaning rather than their wording, then reranked for precision.
Analogy Mental model
Scoring every candidate on fifteen hundred dimensions and plotting them in space. Whether two people used the same vocabulary matters less than whether their points sit close together.
In practice What you actually do
No mathematics required, only one writing rule: a paragraph must be about one thing. A passage that discusses descaling the machine, last year's company retreat, and the returns policy averages into a vector that sits near nothing and is retrieved for nothing.
Where people get burned Pitfalls
One concept per block, consistently applied, is what makes each vector sharp. Mixed paragraphs are the most common reason good content never surfaces in retrieval.
This entry was written in English from the Chinese original, not machine-translated. Read the Chinese version →