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The advent of ChatGPT in November 2022 showcased the potential of Generative AI powered by Large Language Models (LLMs). The technology allows for the automation of various tasks, including professional quality text, images, audio, video, and programming code.

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• Organizations need to train and prompt the latest innovations in commercial and open source LLMs with their own data. This data, sometimes proprietary to the organization, and some public but fresher than that used to train the original base models, provides the context necessary to craft AI-generated outputs relevant and differentiating to the business.
• To prompt AI models with our own data, we need to turn it into vector embeddings. Vector embeddings provide multi-dimensional numerical encodings of data that capture its patterns, relationships, and structures.
• Once our data has been transformed into vector embeddings, it is supported and indexed in a vector store such as MongoDB.
• The store is queried with an ApproximateNearestNeighbor (ANN) algorithm toperform a KNearestNeighbor (KNN) search using an algorithm such as 'HierarchicalNavigableSmallWorlds' (HNSW).

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