Traditional retrieval augmented generation works by chunking documents, embedding them into a vector database, and at runtime embedding the user query, running a semantic similarity calculation to retrieve the top N relevant chunks, and passing those plus the original query to the language model as context.
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Adam LucikEvidence Quote
“taking your document you chunk it into workable pieces of text that is all embedded into a vector database using an embedding model and then at runtime what happens is someone inputs a query that query is also embedded a semantic similarity calculation is ran across your vector database”
Created: 6/18/2026, 1:58:48 PM
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