Embeddings' Impact on Retrieval Accuracy in RAG Systems

In Retrieval-Augmented Generation (RAG) systems, the quality of embeddings directly influences recall and precision. Embeddings that capture semantic similarity effectively improve recall by retrieving more relevant documents, while fine-grained embeddings boost precision by filtering out noise. However, trade-offs exist: high-dimensional embeddings may overfit, reducing generalization. Optimal performance requires balancing embedding quality with retrieval algorithms, such as dense passage retrieval.