AI & Search Wiki Entry

Retrieval Augmented Generation (RAG)

A technique combining retrieval from a knowledge base with LLM generation.

RAG systems retrieve relevant documents from indexed sources at query time and pass them to an LLM as context for answer generation. Major AI search engines use RAG architectures to deliver up-to-date, citable answers. Optimising for RAG involves making content easily retrievable through structured signals, clear formatting and authoritative sourcing.

Related Entries

Generative Search

Search interfaces that use large language models to generate synthesised answers.

LLM Optimisation (LLMO)

Optimising content for inclusion in large language model training and retrieval.

Embeddings

Numerical representations of text used by AI systems to measure semantic similarity.

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