embedding-strategies

To choose the best embeddings for RAG, compare domain-specific models using precision@k, recall@k, MRR, and nDCG. Evaluate prose, code, finance, or legal models based on token limits and dimensions to optimize semantic search relevance.

The best way to chunk documents for semantic search is applying overlap-aware chunk sizing alongside text preprocessing and normalization. This optimizes the embedding pipeline to improve retrieval quality and prevent missing context during vector search.

Benchmark vector search quality for multilingual content by iteratively comparing embedding models using retrieval metrics like MRR and nDCG. Select models designed for multilingual domains and evaluate them against your specific document types and target latency.

Vector dimension reduction can improve RAG relevance by optimizing storage and retrieval latency. Evaluate dimension reduction options during embedding pipeline optimization to balance retrieval speed against semantic search accuracy using benchmarking metrics.

RAG retrieval returns irrelevant results due to poor embedding model selection and improper chunking. Fix this by tuning overlap-aware chunk sizing, applying text normalization, and benchmarking new models using precision@k and recall@k metrics.