Retrieval-Augmented Generation

RAG fundamentals

Retrieval-augmented generation (RAG) is a technique that improves large language model responses by retrieving relevant information from external sources before generating an answer. These sources may include databases, uploaded documents, web pages, knowledge graphs, or frequently updated organizational data. The retrieved content is added to the user's prompt, enabling the model to use domain-specific or current information that may not have been present in its original training data. RAG was formally introduced in a 2020 research paper combining a parametric language model with a non-parametric external memory accessed during inference. A typical RAG pipeline converts source documents into embeddings, stores them in a vector database, retrieves documents relevant to a user query, and supplies the retrieved context to the language model for generation. RAG can improve factual grounding, transparency, and updateability while reducing the need for frequent model retraining. However, it does not eliminate hallucinations: models may misinterpret context, combine conflicting or outdated sources, retrieve misleading content, or answer confidently when evidence is insufficient. Retrieval quality, chunking, reranking, hybrid search, source evaluation, and uncertainty handling are therefore important to reliable RAG systems.

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External knowledge and model grounding

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