Retrieval-Augmented Generation (RAG)

Let's start with the easy one, folks.

Retrieval-Augmented Generation (RAG) is an advanced generative technique designed to enhance the capabilities of language models. It achieves this by augmenting the generation of a response from the original training data with the retrieval of relevant information from a knowledge base (e.g., the internet, a local database).

This dual approach allows RAG systems to leverage the extensive general knowledge embedded in large language models while also accessing specific, up-to-date information that may not be present in the model itself. By doing so, RAG improves the accuracy and relevance of generated responses, making it particularly useful in applications where current and precise information is crucial.

See you tomorrow!