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You are here		blog.vespa.ai Pretrained Transformer Language Models for Search - part 1 \| Vespa Blog
\|	\|	unstructured.io Choosing the right embedding model for your RAG application: a comprehensive guide - Unstructured	7.7 parsecs away Travel
\|	\|	Navigate the Massive Text Embedding Benchmark (MTEB) leaderboard with confidence! Understand the difference between Bi-Encoders and Cross-Encoders, learn how text embedding models are pre-trained and benchmarked, and how to make the best choice for your specific use case.	7.7 parsecs away Travel
\|	\|	blog.reachsumit.com Prompting-based Methods for Text Ranking Using Large Language Models - Sumit's Diary	7.8 parsecs away Travel
\|	\|	Welcome to Sumit Kumar's Personal Blog!	7.8 parsecs away Travel
\|	\|	zackproser.com How are embeddings models trained for RAG?	9.3 parsecs away Travel
\|	\|	Embeddings models are the secret sauce that makes RAG work. How are THEY made?	9.3 parsecs away Travel
\|	\|	neptune.ai Building LLM Applications With Vector Databases	81.3 parsecs away Travel
\|		Flexibility provided by being able to retrieve context at runtime is the primary motivation behind using vector databases in LLM applications	81.3 parsecs away Travel