What are embeddings, and what can you build with them besides RAG?

What the interviewer wants to hear

• Vectors that capture meaning; similar items are close
• Semantic search and recommendations
• Clustering, deduplication, classification
• Similarity measured with cosine or dot product

Model answer

Embeddings are lists of numbers that represent the meaning of text, images or other data. An embedding model is trained so that similar items end up close in vector space, and we compare them with cosine similarity or dot product. Besides RAG, I can use them for semantic search, where a query like "laptop will not charge" finds articles about "battery not charging". I can recommend similar products or articles. I can cluster support tickets to find common themes, and detect near-duplicate documents. Embeddings also make good features for simple classifiers: for example, a logistic regression on embeddings can route tickets to the right team cheaply. They are fast and cheap compared to calling a large model for every item, which makes them useful at scale.

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