This chapter delves into the efficiency and limitations of embeddings while discussing the need to move beyond them for more complex operations. It explores the workings of a model that predicts and embeds text prompts, utilizing a vector database to find matching neighbors in an index, leading to source links. The conversation also touches on the potential of training models in embedding space and leveraging knowledge graph approaches for high-value results in web search.

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