AI / LLM Basics Interview Questions
How are Embeddings used beyond text generation?
Beyond powering the internal workings of an LLM, embeddings are widely used on their own as a tool for comparing the meaning of different pieces of text.
- Semantic search: finding documents whose meaning is similar to a query, not just ones sharing the same keywords
- Clustering: grouping similar pieces of text together automatically
- Recommendation: finding items whose descriptions are semantically similar to something a user already liked
These applications rely on the same core idea that makes embeddings useful inside a model: pieces of text with similar meaning end up mathematically close together, which is exactly what similarity search and clustering need.
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