Conclusion

Conclusion

Retrieval-Augmented Generation is a technique that enhances the capabilities of LLMs with custom data. It’s a useful and practical way of personalizing AI. It works with unstructured and structured data to improve LLMs and other AI applications.

Embeddings transform data into numerical vector representations and organize them in a manner relevant to the given context. They make it possible to store all kinds of data from text to images and video in a manner that provides advanced retrieval capabilities. Different types of embeddings exist to provide efficient data retrieval based on the given use case.

Vector databases are specialized databases designed to store and efficiently manage the vector data generated by embeddings. Their scalability lets them handle large datasets, making them indispensable for RAG systems that process substantial amounts of information.

JupyterLab is an interactive programming platform that forms part of the data science computing tools from Jupyter Project. It’s free, open-source, and an excellent choice for building a RAG application which you’ll do by the end of this module.