From the course: Secure Generative AI with Amazon Bedrock

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Datasets and vector embeddings

Datasets and vector embeddings

From the course: Secure Generative AI with Amazon Bedrock

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Datasets and vector embeddings

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- [Instructor] Although foundation models can generate human-like text, images, audio, and more from your prompts, this might not be sufficient for enterprise use cases. To power customized enterprise applications, the foundation models need relevant data from enterprise data sets. Enterprises accumulate huge volumes of internal data, such as documents, presentations, user manuals, reports, and transaction summaries, which the foundation model has never encountered. Ingesting and using enterprise data sources provide the foundation model with domain-specific knowledge to generate tailored, highly relevant outputs that align with the needs of the enterprise. You can supply enterprise data to the foundation models as context along with the prompt, which will help the model to return more accurate outputs. But how do you figure out the context to pass? For that, you need a way to search the enterprise data sets using the prompt text that is passed. This is where vector embeddings help…

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