Vector databases power the retrieval layer in RAG workflows by storing document and query embeddings as high‑dimensional vectors. They enable fast similarity searches based on vector distances.| AIMultiple
I have been relying on SQL for data analysis for 18 years, beginning with my days as a consultant. Translating natural-language questions into SQL makes data more accessible, allowing anyone, even those without technical skills, to work directly with databases.| AIMultiple
The effectiveness of any Retrieval-Augmented Generation (RAG) system depends on the precision of its retriever component.| AIMultiple
RAG (Retrieval-Augmented Generation) improves LLM responses by adding external data sources. We benchmarked different embedding models with various chunk sizes to see what works best.| AIMultiple