Embeddings and Vector Search in LLMs

HardGenerative AI & LLMs00:00

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Problem

How do embeddings and vector search work in the context of LLMs?

Explain how text becomes vectors, how similarity is calculated, and how vector search supports retrieval-augmented generation. Include the main engineering tradeoffs, such as chunking, embedding model choice, index selection, recall, latency, and cost. Describe how you would evaluate retrieval quality and reduce hallucinations when retrieved content is incomplete or irrelevant.