Improving RAG Retrieval by 60% with Fine-Tuned Embeddings

With an estimated 50% or more of enterprise AI applications relying on Retrieval Augmented Generation (RAG) architectures, knowing how to optimize your retrieval pipeline is more important than ever. In many ways, retrieval is the unsung hero of RAG as surfacing high-quality, relevant documents is essential for generating accurate and contextually rich responses. Even the most powerful language models are only as good as the domain specific data they use as context. To improve my own RAG pipelines, I rely heavily on fine-tuning embedding models with synthetically generated, domain specific data, and use dimensionality reduction techniques to ensure the representations are both fast to compute and highly effective for retrieval.