What is an embedding?
An embedding is a numerical representation of text that captures its meaning, allowing passages with similar meaning to be found even when they share no words.
Embeddings are why an AI representative can answer "do you deliver to Berlin?" from a page that only says "we ship across the EU". Text is converted to vectors, and similar meaning lands close together.
Why it matters
Keyword search fails on paraphrase, which is how customers actually write. Embeddings make retrieval robust to phrasing, which is the difference between a bot that usually finds the answer and one that usually doesn't.
Examples
- "Refund window" retrieving a page that says "you have 30 days to return".
- A question in Spanish retrieving relevant English content.
- Grouping near-duplicate customer questions to spot repeats.
Related
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What is RAG (retrieval-augmented generation)?
RAG is what separates a chatbot that knows your business from one that guesses. Documents are split into passages and indexed by meaning; each question retrieves the closest passages, and the model is instructed to answer only from them.
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What is a knowledge base?
Traditionally a knowledge base is a help centre humans read. For AI support it is the source material a representative answers from — which means an ordinary website, well written, can serve as one without a separate help centre.
term
What is RAG (retrieval-augmented generation)?
RAG is what separates a chatbot that knows your business from one that guesses. Documents are split into passages and indexed by meaning; each question retrieves the closest passages, and the model is instructed to answer only from them.
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What is chunking in AI retrieval?
A whole page is the wrong unit to retrieve. A returns policy page might cover eligibility, timing, refunds and exchanges; retrieving all of it to answer one question about timing buries the relevant sentence in three paragraphs of noise. Chunking splits it so that each part can be found on its own. The split usually follows paragraph or heading boundaries, because those are where a human author already decided one idea ended and another began.
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What is semantic search?
Keyword search asks whether two pieces of text share words. Semantic search asks whether they mean the same thing. The difference shows up immediately in customer questions, because customers do not use a company's vocabulary: someone asks 'can I send it back?' about a page headed 'Returns and exchanges', and the two share not one significant word.
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What is a vector database?
Comparing a question to every stored passage is exact and gets slower in proportion to how many passages there are. A vector database trades a little accuracy for a large speed gain by building an index that skips most of the comparisons. That trade is worth making at millions of vectors and is close to pointless at thousands.
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