In the last few years, embeddings have become one of the most essential building blocks in AI systems, most especially in Search, Recommendation Engines, and Retrieval-Augmented Generation, or RAG. If you have worked with semantic search, vector databases, or LLM-powered assistants, you have already relied on embeddings, whether you know it or not.
In this blog, we’ll break down:
What embeddings really are
Sparse vs Dense Embeddings
How Embedding Retrieval Works
What embedding-based retrieval means in practice
What are embeddings in RAG
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