A RAG system can still hallucinate when the correct passage is not retrieved, the retrieved context is incomplete, the source is outdated, or the model generates claims that go beyond the supplied evidence.
Embeddings are the numerical representations that help RAG and semantic search systems find information by meaning rather than exact keyword matches. Here’s how they work, where they fail, and how to build better retrieval.
Document chunking determines what a RAG system can retrieve. Learn how to choose chunk boundaries, preserve context, compare chunking strategies, and evaluate retrieval quality.