How to Choose the Best Embedding Model for Your LLM Application

In 2025, the concept of "embeddings" has gained significant attention in the development of generative AI applications, particularly in retrieval-augmented generation (RAG) systems, which enhance large language models by retrieving data from external sources. Embeddings, which are vectors representing text or other data forms, are crucial for semantic search in these systems, allowing semantically similar entities to be mapped closely in vector space. The tutorial discusses how to choose the best embedding model for RAG applications, emphasizing the importance of benchmarking models using the Retrieval Embedding Benchmark (RTEB) Leaderboard on Hugging Face and evaluating them on specific datasets to find the best fit for particular use cases. Three models—VoyageAI's voyage-3-large, Google's gemini-embedding-001, and OpenAI's text-embedding-3-large—are evaluated on criteria such as embedding latency and retrieval quality, with voyage-3-large emerging as the top performer due to its balance of speed and accuracy. The tutorial also highlights the importance of considering cost, latency, and retrieval quality when selecting an embedding model for production environments.