AI
Embeddings
Numerical representations of text, images, or other data that capture semantic meaning.
Why it matters
- Enable semantic search and similarity matching
- Foundation for RAG and recommendation systems
- Allow machines to understand meaning, not just keywords
When to use
- For semantic search functionality
- When building RAG systems
- For content recommendation engines
Common mistakes
- Using wrong embedding model for the domain
- Not normalizing or preprocessing input text
- Ignoring embedding dimension tradeoffs
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