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What is RAG?#
During the course, I was introduced to Retrieval-Augmented Generation (RAG) as a way to improve how Large Language Models generate answers.
One of the main problems with LLMs is that they can sometimes produce incorrect or outdated information. RAG helps with this by retrieving relevant information from an external source before generating a response. This means the model does not rely only on its built-in knowledge, but can also use specific documents or data as context.
I found this approach especially interesting because it makes chatbot systems more reliable and more useful in practice. It also creates a clearer separation between the language model and the knowledge source, which makes the system easier to update and maintain.