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.
What does semantic search find using embeddings?
What core idea do these applications rely on?
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