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Explainer videoJohannes Hötter
RAG, sketched
Retrieval-Augmented Generation as an animated sketch: documents, chunking, embeddings, retrieval, and an answer.
Documents
We start with your documents: manuals, support tickets, and wiki pages.
Chunking
We split them into sections that can meaningfully answer a question.
Embeddings
Each section becomes a vector, placing it in a space that represents meaning.
Retrieval
The question becomes a vector too. We retrieve the sections with the most similar meaning.
Answer
The model answers using this context and can cite its sources.
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