Fine-tuning embedding models

When building LLM applications based on retrieval-augmented generation (RAG), the quality of embeddings directly impacts both retrieval of the relevant documents and response quality. Fine-tuning embedding models can enhance the accuracy and relevance of embeddings for specific tasks or domains. Our teams fine-tuned embeddings when developing domain-specific LLM applications for which precise information extraction was crucial. However, consider the trade-offs of this approach before you rush to fine-tune your embedding model.