How RAG Helps AI Models Answer Questions Beyond Their Training Data

Large language models have broad but frozen knowledge, making them unable to address company-specific or post-training information without extra help. Retrieval-Augmented Generation (RAG) solves this by giving the model a search tool that fetches relevant documents and feeds the results back into its context window. Rather than relying on basic full-text search, RAG converts documents into mathematical vectors called embeddings, which capture meaning so that semantically similar text can be matched even when exact words differ. Because a single document may cover multiple topics, it is split into smaller chunks before embedding, improving search precision and reducing unnecessary context bloat. This article, part of a series on transitioning from software to AI engineering, focuses on the agentic RAG variant where the model itself decides what to search for.