Large language models have knowledge cutoffs and lack access to private data, a problem solved by providing relevant context at query time using RAG.
RAG relies on similarity search, not keyword search, which captures meaning by converting text into numerical representations called vector embeddings.
A basic vector embedding can be created by tokenizing text, building a vocabulary of unique words, and representing each document as a vector of word counts.
Cosine similarity measures the angle between two vectors to determine their semantic closeness by focusing on direction (meaning) rather than magnitude.
The simple bag-of-words model is sensitive to vocabulary, slow to scale, and fails to capture nuanced semantic meaning like word order or synonyms.
Production RAG systems use sophisticated embedding models and specialized vector databases for efficient, accurate, and scalable similarity search.
Beyond basic similarity search, techniques like ColBERT and knowledge graphs can improve retrieval accuracy, and vector search can power features like related content recommendations.
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Ismaning, Germany
26:25Ashish Sharma
21:17Carl Lapierre
29:11Michael Hunger
26:54Duan Lightfoot
28:20Dieter Flick
46:51
24:42Martin O'Hanlon
31:12Daniel Töws
Jobs that call for the skills explored in this talk.
The Rolewe
Charing Cross, United Kingdom
Syncreon
Cleveland, United States of America
Collaboration Betters The World GmbH
Ritwik Infotech
Bramkas Inc.
Dallas, United States of America
Stack Infrastructure
Denver, United States of America
