RAG 2.0

Retrieval-Augmented Generation (RAG) is the gold standard for connecting LLMs to private data, and the RAG 2.0 category explores the cutting edge of this technique. We discuss the engineering of vector databases, the nuances of semantic search, and the implementation of advanced retrieval strategies that minimize hallucinations. This space is dedicated to developers building intelligent knowledge bases, enterprise search engines, and AI systems that can cite their sources with pinpoint accuracy in real-time environments.