Semantic search and retrieval-augmented generation (RAG) serve different but complementary purposes in AI systems. Semantic search ranks documents by meaning using vector embeddings and similarity metrics, making it ideal for finding relevant information quickly. RAG extends this by retrieving context from external knowledge
Table of contents
What is semantic search?What is RAG (retrieval-augmented generation)?How does semantic search work?How does RAG work?What is the difference between semantic search and RAG?What problems does semantic search solve?What problems does RAG solve?Can semantic search be used without RAG?Can RAG work without semantic search?What are the pros of semantic search?What are the pros of RAG?What are the limitations of semantic search?What are the limitations of RAG?How is semantic search evaluated for effectiveness?How is RAG evaluated for effectiveness?What are the latest trends in semantic search and RAG?Frequently Asked Questions (FAQs)Harnessing the power of precisionSort: