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AI & Machine Learning · Foundational

Retrieval-Augmented Generation (RAG)

Retrieving relevant information from external sources at query time and adding it to the prompt so the model answers from it.

Definition

A RAG pipeline ingests and chunks documents, embeds them into a vector index, retrieves the most relevant chunks for each query and passes them to the LLM as context. It grounds answers in current, proprietary data and enables citations without retraining. Quality depends heavily on chunking, retrieval and reranking.

Example

A help-desk bot retrieving the latest HR policy pages before answering an employee's question.

Exam tip

Choose RAG when data changes often or must be cited; choose fine-tuning for style or behavior.

Don't confuse it with

Related terms