Retrieval-Augmented Generation (RAG) Explained: Vector DBs and Semantic Search

Why LLMs Need Vector Search

Training an LLM on your internal company data is expensive and quickly becomes obsolete. Retrieval-Augmented Generation (RAG) solves this by fetching relevant document snippets at query time and injecting them into the prompt.

Embeddings and Semantic Cosine Distance

Vector embeddings represent conceptual meaning rather than keyword strings, allowing search queries to find conceptually relevant documents effortlessly.