Jump go di content
Glossary

Retrieval-Augmented Generation (RAG)

Retrieval-augmented generation na technique wey ground language model output in retrieved source documents instead of relying only on im parametric memory. Di system fetch relevant passages from knowledge base, supply dem as context, and ask model to answer using only dat evidence.

Synonym dem: RAG, retrieval augmented generation, grounded generation, context augmentation

Retrieval-augmented generation separate wetin model know from wetin dem train am on. At query time retriever pull most relevant chunks from maintained knowledge base, and model compose answer constrained to dat evidence. This reduce hallucination, make answers auditable through citations, and let teams update knowledge without retraining. For governed setting, RAG na mechanism wey turn question into grounded, cited response wey person or policy fit verify before trust.

Question dem wey people dey ask well-well

Why use RAG instead of fine-tuning?
RAG keep knowledge inside external store wey you fit update instantly, so answers stay current and every claim fit trace to source. Fine-tuning bake knowledge into weights, which slower to refresh and harder to attribute.
Wetin RAG pipeline include?
Usually ingestion and chunking, embedding, index for vector or hybrid search, retriever, and generation step wey condition model on retrieved passages and return cited evidence.