Retrieval Augmented Generation is a method in which a system retrieves relevant documents or facts from an external knowledge source and injects them into the prompt or context used by a generative model.

Key Points

RAG retrieves relevant context before generation.
It improves factual grounding for questions that depend on current or specialized information.
It is commonly used with vector databases and language models.
RAG helps reduce hallucination risk by anchoring outputs in retrieved source material.
It is a common building block for AI agents and knowledge assistants.

Concept

RAG adds retrieval to generation so the model can answer from a current or domain-specific evidence set instead of relying entirely on what was learned during training. A request is first mapped to relevant documents, passages, or records, often using embeddings and a vector database, and then those results are supplied to the model for response generation. This pattern is especially useful where the knowledge base changes frequently or where accuracy depends on the operator's own corpus rather than general training data.

Explainer

For ConnectedEarth, retrieval augmented generation is a strong fit for operational knowledge access because the useful answer often lives in a specific corpus: procedures, incident history, asset notes, or domain references. RAG helps an AI agent answer with the right context rather than a generic model guess. The downside is that retrieval quality becomes a dependency: if the search step misses the right source or pulls weak context, the generated answer can still be wrong.

Cite this entry

Retrieval Augmented Generation. ConnectedEarth Knowledge Base. https://connectedearth.space/knowledge-base/retrieval-augmented-generation/ (updated 13 September 2026).