Home Glossary Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) - Page 10

Retrieval-augmented generation, or RAG, combines a generative model with a search step that supplies relevant information at request time. A system converts the query into a searchable representation, retrieves passages from approved sources, and includes them as context for the model’s response. This approach can provide current or organization-specific facts without retraining the model and can support citations. Quality depends on document preparation, indexing, permissions, retrieval ranking, context limits, and the model’s ability to use evidence correctly. RAG reduces some unsupported claims but does not eliminate them: sources may be outdated, relevant material may be missed, and the model may still distort or ignore retrieved information.

Andrew Tulloch Leaves $12B AI Startup to Join Meta After Turning Down $1.5B Offer
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AI & Machine Learning, Immersive Reality (AR, VR, MR, and XR), News, Startups & Investment

Andrew Tulloch Leaves $12B AI Startup to Join Meta After Turning Down $1.5B Offer

By • 3 mins read

Andrew Tulloch, co-founder of the $12 billion AI startup Thinking Machines Lab, has joined Meta after previously rejecting what reports described as a $1.5 billion offer — a figure Meta has since called ‘inaccurate and ridiculous.’