AI Techniques

RAG (Retrieval-Augmented Generation)

A technique that enhances LLM responses by retrieving relevant information from external knowledge bases before generation. RAG reduces hallucinations and enables access to current or proprietary information.

Related terms

Vector Database
A database optimized for storing and querying high-dimensional vector embeddings via approximate nearest-neighbor search. Common choices include Pinecone, Weaviate, Qdrant, Milvus, and pgvector. Central to most RAG architectures.
Embeddings
Numerical vector representations of data (text, images, etc.) that capture semantic meaning. Similar items have similar embeddings, enabling semantic search and clustering.

Also referenced: Knowledge Base.

Framework dimensions

Free tools

Whitepapers

LLM Cost Optimization Patterns

Comparisons

RAG vs Fine-tuning vs Prompt Engineering: Decision Framework

Next steps

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Where this fits

RAG (Retrieval-Augmented Generation) is part of the AI Techniques vocabulary used in the Generative AI Maturity Framework. See the full glossary for the complete set of 149 defined terms, or take the free maturity assessment to see where your organisation stands.